commodity risk management - oliver wyman

12
Global Risk Center IN PRACTICE GUIDE SIX STEPS TO ASSESS COMMODITY RISK EXPOSURE

Upload: others

Post on 17-Jan-2022

4 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Commodity Risk Management - Oliver Wyman

global Risk Center

IN PRACTICE GUIDEsix stePs to assess commoditY risK exPosure

About this RepoRt

this report and the associated workbook provide guidance for companies to better

understand and quantify the impact of commodity risks on earnings in order to improve an

organizationrsquos commodity risk management

the supporting excel workbook is available by clicking on the pushpin icon this guide is a

companion piece to ldquoVolatility not Vulnerabilityrdquo published by the Association for Financial

professionals (AFp) and oliver Wyman in october 2011 the article is available by clicking on

the pushpin icon or can be found online at wwwoliverwymancom

CoRpoRAte ChAllenges in mAnAging Commodity Risk

bull developing management strategies (pricing procurement and hedging) using a holistic risk-return perspective instead of just heuristics to make decisions

bull evaluating portfolios of potential mitigation strategies which have impact on internal diversification and aggregation in adequate depth

bull Automating the aggregation and integration of a wide range of data and assumptions (eg purchasing contracts commodity forecasts ability to pass-through)

bull Applying sufficient tools to engage suppliers on contract terms using a risk-return perspective and to develop innovative structures that are mutually beneficial

bull engaging champions and senior sponsors who are passionate about implementing long-term risk mitigation strategies such as revised contract structures or pricing changes

bull treating commodity price risk management as a ldquomargin stabilizationrdquo process rather than a ldquocostrdquo

Global Risk Center

VOLATILITY NOT VULNERABILITYTO SURVIVE SWINGS IN COMMODITY PRICES COMPANIES MUST FIRST BUILD A RISK-MANAGEMENT FRAMEWORK

Historically most businesses could simply withstand changes in commodity prices given that the swings were usually temporary cyclicalmdashand manageable However structural changes in the global economy are creating wilder swings in commodity prices that are not only affecting short-term profits but also long-term planning and investment In this environment every company must develop a formal risk management approach to counter the growing volatility in commodity prices For corporate leaders this means building the infrastructure governance programs and analytical capabilities that can help them better manage their exposure to commodities

1 COMMODITY PRICES AND VOLATILITY

After a brief respite in 2009 the volatility that has come to define many commodity markets

roared back in the latter half of 2010mdashmuch to the dismay of businesses for whom oil

industrial metals and other raw materials comprise a significant share of their costs Crude

oil cracked the psychological barrier of $100 per barrel major food price indexes reached

record highs in the first quarter of 2011 and base and industrial metals such as copper and

aluminum also reached new highs mdashcreating significant pain for buyers (see Exhibit 1) Even

where prices pulled back the cost of many commodities remained higher than beforemdashthe

result of structural shifts in supply and demand on both a local and global scale (For a closer

look at the causes of volatility see Exhibit 2)

These commodity shocks are not only cutting into corporate profits but are testing the

abilities of even the best businesses to plan for and invest in the future Already in 2011 the

CEOs of consumer-facing companies as diverse as PepsiCo Kraft Kimberly-Clark and Levirsquos

have said they expect commodity price inflation to pose a significant challenge to continued

earnings growth over the next several years Those in the middle of the value chain have a

tougher challenge as they cannot easily raise prices to recover lost margins Few expect this

storm to pass quickly The 2011 World Economic Forum Global Risks Survey found corporate

executives in agreement that a key risk they face in the coming decade is extreme volatility in

energy and other commodity prices1

1 World Economic Forum ldquoGlobal Risks 2011rdquo 6th edition January 2011 Oliver Wyman was a contributor to that report

EXHIBIT 1 COMMODITY PRICES ARE SOARING INDEX

8

12

10

6

4

2

0

14

MULTIPLE OF INITIAL PRICEREAL PRICE HISTORY

1987 1991 1995 2003 20111999 2007

Crude Oil

Copper

Wheat

source Datastream 2011

Copyright copy 2011 Oliver Wyman 3

ExhIBIT 2 WhY ThE VOLATILITY

CAUSE DESCRIPTION

Erratic weather Catastrophic weather events have affected production (2009 drought in Australia affected global wheat prices)

Emerging markets Growth in emerging markets has increased demand for food energy and raw materials Global food output will have to rise 70 by 2050 to meet demand while global energy demand is predicted to increase by 36 between 2008-2035

Speculation Emergence of commodities as an investment class (development of commodities-based ETFs)

Infrastructure spending Deteriorating infrastructure in developed countries and a lack of infrastructure in emerging economies hampers the physical flow of commodities

Supply-country strategies Development of market-distorting trade policies (Russian wheat export ban followed crop shortfall in 2010)

Purchasing-country strategies

Countries developing more sophisticated buying capabilities (Korea opened a grain-trading office in Chicago in 2011)

Food and Agriculture Organization of the United Nations World Summit on Food Security November 2009

International Energy Agency World Energy Outlook 2010

This volatility could persist for years particularly given that governments appear willing to disrupt or intervene in markets including halting exports These collective forces have created pressure for corporations to develop strategies to mitigate this growing risk While companies in agri-processing oil refining and wholesale electricity generation have developed formal approaches to trading and risk management programs the majority of companies need to do moremdashmuch more

Given the likelihood of further volatility in commodity prices every company must adopt an analytic risk framework based on a clear understanding of its exposure This paper offers guidance on developing a plan for managing commodity risks that draws on the best practices of companies from around the world The approach is built around the three pillars of a ldquobest practicesrdquo program governance infrastructure and analytics The paper provides an in-depth review of the role of analytics and outlines a new systematic approach to managing commodity risk illustrated through case studies

ONE COMPANYrsquoS MOVES TO TAME ITS VULNERABILITY TO VOLATILITY

A leading industrial company failed to deliver its projected earnings due to a decline in the profitability of non-core energy activities To measure the companyrsquos total exposure to energy volatilitymdashand quantify the potential effect on future profits it needed to develop an integrated energy risk profile In other words the company needed the ability to aggregate the energy exposure of each business unit and evaluate the effectiveness of existing mitigation efforts before it could truly understand net exposure

The company analyzed commodity volatilities and correlations to produce a probabilistic analysis and derive ldquoEPS-at-riskrdquo estimates demonstrating the portion of earnings that were vulnerable to these price volatilities It then adopted a risk assessment tool capable of performing scenario analysis linked to alternative market states and specific events integrating this discipline by training both operational and financial staff in Treasury Procurement Planning and Operations This helped the company to gain insights into the aspects of its energy exposure that were most sensitive to price movements under different future states and thus were targets for mitigation efforts

Copyright copy 2011 Oliver Wyman 4

2 COMMODITY RISK MANAGEMENT FRAMEWORK

DEVELOPING A COMMODITY RISK GOVERNANCE PROGRAM

A crude oil producermarketer in Eastern Europe was recently seeking to enhance margins by creating regional physical trading capability First the Board required stakeholder alignment on a multi-dimensional risk appetite statement to guide risk and scale considerations High level loss limits were linked to the risk appetite and from these ldquodesk-levelrdquo limits were calculated By codifying the risk limits in policies the equity usage growth targets and working capital requirements were made explicit and served to support all later decisions on the business expansion plan

A structured commodity risk management framework is built around three pillars

Governance Infrastructure and Analytics Together these pillars support both short-

term tactical decisions and long-term strategic initiatives (see Exhibit 3)

GOVERNANCE

The first pillar of this framework is Governance in which an organization identifies the main

objectives of its commodity risk management program Companies first create a risk

appetite statement In this critical step an organization defines its risk tolerance and

aligns it with its broader performance objectives The risk appetite statement thus

establishes the basis of the organizationrsquos risk limits

ExhIBIT 3 COMMODITY RISK MANAGEMENT FRAMEWORK

SHORT-TERM TACTICAL DECISION MAKING

LONG-TERM STRATEGIC DECISION MAKING

GOVERNANCE

bull Risk appetite and corporate objectivesbull Risk limits bull Risk policybull Delegations of Authoritybull Decision frameworks

INFRASTRUCTURE

bull Reportingbull Accountingbull ITsystemsbull Organizational designbull Human capital

ANALYTICS

bull Price forecastingbull Commodity exposure modeling bull Stress and scenario testingbull Hedge optimization

Copyright copy 2011 Oliver Wyman 5

INFRASTRUCTURE

The second pillar Infrastructure comprises the organizational structure systems and

human capital that companies need to measure and manage risks Organizations need to

assess whether they have the systems to capture and monitor the necessary data flows The

organizational structure is important because the volatility in commodity markets requires

discipline and a tight alignment between managing cost- and revenue-related risks across

the enterprise historically these functions worked independently but today that results in

inefficiencies or even conflicting actions by different internal teams human capital is also

key In some cases a corporation may not have the experience or capabilities to manage

commodity risks effectively While a company can outsource risk management to financial

institutions or other market participants it pays a significant premium for that service and

potentially forfeits any upside potential In the process it gives its ldquopartnerrdquo proprietary

information they can use to trade for solely their own benefit

INCREASING EFFECTIVENESS ThROUGh IMPROVED INFRASTRUCTURE

The effects of steady growth were slowly compromising the risk management effectiveness of a major North American crude and distillates marketertrader Reporting timeliness was eroding error rates were climbing and managementrsquos confidence in risk control was decreasing prompting a full review of the organizational design and processes This revealed that the risk control function (middle office) was still performing much of the trade reconciliation that trade details were transmitted by email and that most reporting was done through spreadsheets

Several straightforward organizational shifts and systems enhancements eliminated a number of bottlenecks and enabled risk reporting down to the cargo level by desk and counterparty

ANALYTICS

The third pillar of a commodity risk management program is Analytics Organizations

cannot assess their financial exposure to commodity price swings without robust analytical

tools These analytics (or ldquomodelingrdquo) platforms support decision making at every level

mdash by helping managers model the future paths of commodity prices conduct stress- and

scenario-testing and evaluate and optimize risk-return profiles using a range of price-risk

management strategies however analytical capabilities vary dramatically from firm to firm

Companies should take a sequential approach to developing a robust analytics framework

that will support commodity risk management

Copyright copy 2011 Oliver Wyman 6

ThINK LIKE A TRADER

Trading is viewed as a necessary evil by some corporations given the connotations of excessive speculation and risk-taking with derivatives (in this case commodity futures contracts) A recent review of annual reports reveals that many companies disclosing commodity positions caution that these are strictly for hedging purposes and that the company ldquodoes not hold financial derivative positions for the purposes of tradingrdquo

Whether they realize it or not many companies are in fact speculating on commodity pricesmdashon both the cost and revenue sides Procurement officers are tasked with getting the best price on purchases for the company and its business units So the procurement staff commonly enters into forward fixed-price arrangements to guarantee both supply and pricing Just like traders they have thus made a bet on future prices and concluded price risk management with that view in mind

Companies need to accept that the days of a ldquoset it and forget itrdquo approach to risk management are over That approach was fine when volatility was low and prices increased gradually over time Today companies need to empower designated executives to think like traders ldquoIs this a good or bad price Should I buy more or run down inventoryrdquo Since most companies arenrsquot traders by nature they need to create a trading playbook that is integrated with their overall game plans

First a company must understand its commodity risk profile This helps the organization

assess its net exposure to commodity pricesmdashand their inevitable volatilitymdashacross

business and customer segments Detailed analysis also allows the organization to then

develop long-term strategies to mitigate commodity-related risks

A commodity risk profile provides a common understanding for senior management and

a fact-based foundation for evaluating the effectiveness of risk-mitigation actions With

this knowledge management can determine if the companyrsquos current commodity risk

exposure is within its risk tolerance and whether it has communicated these expectations

to stakeholders This analysis also helps the management team evaluate different risk

management strategies It promotes risk mitigation at the portfolio level which helps

reveal any risks lurking in individual business units or departments In short this analysis

will allow the company to optimize risk-return positioning

ThE NET ExPOSURE ChALLENGE

Determining true net exposure requires consideration of several factors gross commodity exposure existing risk mitigations foreign exchange flows demand sensitivity and the interactions among these This can be challenging for an enterprise with wide-spread operations or multiple product lines Typically each factorrsquos net impact at the corporate level is first assessed and any offsets across factors can then be accounted for

Copyright copy 2011 Oliver Wyman 7

To manage commodity price risks in ways that are consistent with broader corporate

objectives companies need a robust set of analytic tools to calculate the current exposure

Exhibit 4 offers a six-step analytical approach that companies can use to determine the

effect of commodity price risks and mitigation efforts on key financial metrics

The first step in the analytical framework requires management to build a forecast

of commodity prices using simulations or other techniques The company should

complement these forecasts with an analysis of alternative price outcomes based on ldquostress

eventsrdquo to understand fully how prices may evolve

In the second step the management team estimates the volume of future commodity

purchases across the enterprise In Step 3 this demand forecast is combined with

price projections to determine the firmrsquos gross commodity exposure In Step 4 the risk

management strategies already in place are identified Then in Step 5 these are applied

against the gross exposure to generate a ldquonet commodity exposurerdquo for the enterprise The

process ends with Step 6 the creation of a holistic company-wide risk profile ndash with the

sensitivities in price projections identified in Step 1 used to explore the potential impact on

EBITDA debt covenants and other financial metrics

Copyright copy 2011 Oliver Wyman 8

ExhIBIT 4 CREATING A hOLISTIC COMMODITY RISK PROFILE

bull Use analytic engines to simulate potential commodity price pathways (data driven)

bull Incorporate market context and paradigm shiftsscenario analysis ( judgment driven)

bull Integrates historical patterns market intelligence and fundamental analysis

bull Unifies disparate views of expected and highlow commodity price scenarios across organization

bull Incorporates interrelationships and correlations between commodities and currencies

STEP ANALYSIS BENEFITS

bull Centralizes commodity requirements across business units and geographies

bull Enables testing of alternative commodity purchase requirements using price elasticity analysis

bull Determine expected commodity purchase volume based on sales expectations

bull Gives context of expected commodity exposure compared to PampL and other risks

bull Accounts for ldquonatural hedgesrdquo in the portfolio

bull Shows shifts due to changes in business mix commodity price expectations

bull Calculate expected commodity exposure (eg price multiplied by volume)

bull Brings together and coordinates dierent functions of the organization (eg Strategy Procurement Treasury) and geographies

bull Sets understanding of risk management options for key commodity exposures

bull Define options for managing commodity price risk

bull Centralize risk management options undertaken across organization

bull Helps management assess the eectiveness of the risk management portfolio vs desired exposure

bull Provides understanding of how commodity price risk flows through the organization

bull Calculate exposure after incorporating current risk management portfolio

bull Enables objective evaluation and comparison of a range of risk management strategies

bull Promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at individual business unit or department level

bull Provides view of the earnings impact from commodity prices at dierent levels of probability

bull Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

COMMODITY PRICEPROJECTIONS

SALES PRICINGANDPURCHASE VOLUMES

GROSS EXPOSURE

RISK MANAGEMENTPORTFOLIO

NET EXPOSURE

HOLISTIC COMMODITYRISK PROFILE

Source Oliver Wyman

Copyright copy 2011 Oliver Wyman 9

IMPROVING COMPETIVENESS WITh A STRATEGIC RESPONSE TO COMMODITY VOLATILITY

A global food ingredient processor was losing sales to much smaller competitors solely because those firms were much more responsive to the highly volatile product price By analyzing the impacts of its forward sales process and market price uncertainty a new strategy was developed to migrate customers to shorter term contracts and to begin linking prices to market indices ndash which immediately improved the firmrsquos competitive positioning

STRATEGIES FOR MANAGING COMMODITY RISK

With a well-defined risk profile and an understanding of its ability to manage risk a

company can build a plan that matches its net exposure to commodities with its tolerance

for risk To manage commodity prices in the short term companies generally have three

tools at their disposal

bull Product pricingmdash identifying customer segments where the company has the ability to

raise prices or create pricing structures that mitigate risk

bull Procurement contract structuringmdashdeveloping innovative risk-sharing contracts with

suppliers

bull Financial hedgingmdashusing financial instruments for hedging to reduce overall

risk exposure

The effectiveness of these short-term strategies depends on the size of the organizationrsquos

exposure to a given commodity mdash and the commodity itself For example financial hedging

works best in commodity markets that are liquid (eg energy products such as crude oil and

natural gas or agricultural products such as wheat and corn) Meanwhile passing higher

commodity costs to customers through price increases is often ineffective in competitive

markets such as consumer products however when commodity cost increases are

significant and widespread pass-through pricing might be more viable Some companies

have taken creative approaches to raising prices For instance a number of consumer

products companies have reduced the volume of productmdashwhile keeping the same

package sizemdashto maintain margins in the face of higher commodity costs Other firms have

substituted cheaper ingredients to lower their net product costs

Over the long term volatility in commodity prices affects the behaviors of consumers

companies and their suppliers In response some companiesmdashand even countries--have

embedded commodity risk strategies into their long-term strategic plans

Copyright copy 2011 Oliver Wyman 10

CONCLUSION

Large and sustained commodity price swings are reshaping whole industries This means

that commodity risk management can no longer be considered the sole responsibility of

the procurement or finance staff With rising commodity prices affecting both the short-

term earnings and long-term strategies it is imperative that C-level executives develop a

deeper understanding of how to mitigate these risks Developing a structured commodity

risk management program built around the components outlined here is crucial Given

the new realities of higher prices and more volatility in commodities organizations must

integrate commodity risk management into their day-to-day operations They also must

build it into their long-term strategies to ensure the viability of the firm itself

ABOUT THE AUTHORS

MICHAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman

Michael J Denton PhD is a New York based Partner in Oliver Wymanrsquos Global Risk amp Trading practice with specialized experience in

energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market

risk modeling portfolio dynamics and risk-based decision frameworks Recent projects have focused on due diligence evaluations

competitive contracting and counterparty credit risk mitigation

ALEX WITTENBERG

Partner Global Risk amp Trading Oliver Wyman

Alex is the Managing Partner of Oliver Wymanrsquos Global Risk Center and has over 20 years of cross-industry experience in risk management

advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision-making and financial performance designing

risk governance for Boards and Management and developing corporate risk monitoring mitigation and transfer frameworks

Copyright copy 2011 Oliver Wyman 11

Copyright copy 2011 Oliver Wyman All rights reserved This report may not be reproduced or redistributed in whole or in part without the written permission of Oliver Wyman and Oliver Wyman accepts no liability whatsoever for the actions of third parties in this respect

The information and opinions in this report were prepared by Oliver Wyman

This report is not a substitute for tailored professional advice on how a specific financial institution should execute its strategy This report is not investment advice and should not be relied on for such advice or as a substitute for consultation with professional accountants tax legal or financial advisers Oliver Wyman has made every effort to use reliable up-to-date and comprehensive information and analysis but all information is provided without warranty of any kind express or implied Oliver Wyman disclaims any responsibility to update the information or conclusions in this report Oliver Wyman accepts no liability for any loss arising from any action taken or refrained from as a result of information contained in this report or any reports or sources of information referred to herein or for any consequential special or similar damages even if advised of the possibility of such damages

This report may not be sold without the written consent of Oliver Wyman

wwwoliverwymancom

Oliver Wyman is a leading global management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development Oliver Wymanrsquos Global Risk Center is dedicated to analyzing increasingly complex risks that are reshaping industries governments and societies Its mission is to assist decision makers in addressing risks by combining Oliver Wymanrsquos rigorous analytical approach to risk management with leading thinking from professional associations non-governmental organizations and academic institutions For further information please visit wwwoliverwymancomglobalriskcenter

The Association for Financial Professionals (AFP) headquartered outside Washington DC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFP is the daily resource for the finance profession

For more information please contact

MIChAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman +16463648423 michaeldentonoliverwymancom

ALEx WITTENBERG

Partner Global Risk amp Trading Oliver Wyman +16463648440 alexwittenbergoliverwymancom

BRIAN T KALISh

Director Finance Practice Lead +13019616564 bkalishafponlineorg

Oliver Wyman
File Attachment
Commodity volatilitypdf

intRoduCtion

given the current and future trends in commodity prices and volatility every company must

better understand its true commodity exposure Companies can often be squeezed by

rising and volatile commodity input prices that cannot be passed along to customers in

their entirety A commodity risk management program can help not all organizations can

or should adopt the sophisticated mechanisms of a pure commodity business however

most organizations particularly those in the middle of the value chain can improve their

commodity risk analytics

exhibit 1 CompAnies ARe ChAllenged by Rising Commodity volAtility

Output prices

bull Competitive pressuresbull Limited ability to pass

through higher costs

COMPANY

bull Understand current forecasts (starting position and price curve)

bull Trace impact of price volatility to earnings

bull Align commodity risk management program strategy with corporate objectives

Commodity risk management program strategy instruments etc

Input prices

bull Increasing volatilitybull Increasing absolute pricebull Breakdown in correlations

the starting point is to understand the companyrsquos holistic commodity risk profile using

analytics and modeling tools A holistic commodity risk profile helps the organization assess

its individual and net exposure to commodity pricesmdashand the inevitable volatilitymdashacross

business and customer segments on a forward-looking basis the profile provides a common

understanding for senior management and a ldquofact-basedrdquo foundation for evaluating the

effectiveness of current risk-mitigation actions and alternative risk management strategies

With this knowledge management teams can determine if current commodity risk exposure

is within the companyrsquos risk tolerance and communicate these expectations to stakeholders

it also helps to promote risk mitigation at the portfolio level by identifying the most

important drivers of overall risk and ensuring the capture of any offsetting risks that may be

present in short this analysis will allow the company to optimize risk-return positioning

this In Practice Guide provides an overview of a stepwise approach and analytic processes

necessary to develop an organizationrsquos net commodity exposure

Copyright copy 2012 oliver Wyman 3

six steps to deteRmining Commodity exposuRe And impACts

exhibit 2 provides an overview of a six-step analytical approach to determine the impact of

commodity price risks on key financial metrics

exhibit 2 six steps to CReAte A holistiC Commodity Risk pRoFile

6 HOLISTIC COMMODITY RISK PROFILE

5 NET EXPOSURE

4 RISK MANAGEMENT

PORTFOLIO

3 GROSS EXPOSURE

2 SALES PRICING AND COMMODITY

PURCHASE VOLUMES

1 COMMODITY PRICE

PROJECTIONS

STEPS

ANALYSISUse analytic engines to simulate potential commodity price pathways (data driven)

Incorporate market context and paradigm shifts scenario analysis (judgement driven)

Calculate expected commodity exposure (ie price multiplied by volume)

Define options for managing commodity price risk

Centralize risk management options undertaken across organization

Calculate exposure after incorporating current risk management portfolio

Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

Determine expected commodity purchase volume based on sales expectations

Copyright copy 2012 oliver Wyman 4

step 1 Commodity pRiCe pRojeCtions

CoRe AnAlysis user-defined baseline commodity prices and volatilities for a predefined set of commodity

inputs are incorporated into a standard simulation process to generate a distribution of

potential future price paths for each commodity

outputs A distribution of terminal price values for each commodity and time horizon

beneFits bull integrates historical patterns market intelligence and fundamental analysis

bull unifies views of commodity price scenarios across the organization

bull incorporates interrelationships and correlations between commodities and other price

risk factors (eg currencies)

the first step in the analytical framework requires

management to build assumptions for commodity

prices in the future using simulation or other techniques

the management team should complement these

forecasts with an analysis of alternative price outcomes

based on ldquostress eventsrdquo to understand fully how prices

may evolve

A simple price simulation model is presented in the

attached workbook (accessed through the pushpin

icon) the inputs for these simulations are located on

the tab labeled ldquointerfacerdquo under the heading ldquoprice

and volatilityrdquo the user defines base case prices and

volatilities for each commodity and forward period

(ie 1st quarter 2nd quarter and so on) the simulated

terminal value prices or outcomes for each commodity

and time horizon are shown on the tab labeled ldquoCmdy

price projrdquo each of these values reflects possible future

outcomes for commodity prices based on three factors

mean price volatility and time

the outputs of the simulations for each commodity are

summarized on the ldquointerfacerdquo page in the ldquoCommodity

price projectionsrdquo section in each case 5th and 95th

percentile outcomes are shown along with the baseline

price scenario over the course of the time period to

provide a richer understanding of possible outcomes

versus a single static forecast not surprisingly the

uncertainty of price forecasts grows with the increasing

time horizon

Copyright copy 2012 oliver Wyman 5

ReadMe

OverviewThe workbook has been prepared as a supplement to the In Practice Guide Six Steps to Assess Commodity Risk Exposure prepared by Oliver Wyman for AFP members and available at wwwafponlineorg The purpose of this workbook is to provide a simple tool for illustration purposes only to better understand and quantify commodity risk impacts on earmings The example focuses on a corporation which utilizes key commodities including natural gas diesel fuel sugar electricity and aluminum The components of the workbook include a primary Interface worksheet as well as support worksheets which house the calculations used to generate outputs on the Interface sheet Note that all user inputs and outputs are exclusively contained in the Interface sheet - all other sheets are for reference only Cells in white in the User Inputs block represent input cells (prices volatilities and hedge designation) Any changes in outputs are generated by using the Calculate button The Interface worksheet receives inputs and provides outputs for a probabilistic earnings forecast model and illustrates the impacts of commodity prices and volatilities on the earnings profile In particular each of the outputs reflects one of the steps in the 6 step process Outputs include(1) Commodity price projections - User-defined baseline commodity prices and volatilities for a pre-defined set of commodity inputs are incorporated into a standard simulation process to generate a distribution of potential future price paths for each commodity(2) Estimated commodity volumes - A predefined set of commodity volumes reflecting commodity demand over time(3) Gross commodity exposure - User-defined baseline prices are combined with simulated price paths and volumes to generate commodity exposure - measured as the difference between the 95th percentile worst case outcome and the baseline expected outcome (4) Net commodity exposure - Gross commodity exposure is adjusted for any financial or physical hedges for each of the commodities Hedges are assumed to lock in the commodity at the baseline expected price Price correlations between commodities are taken into account(5) Holistic commodity risk profile - Commodity exposures are combined with predefined estimates for revenues and other fixed costsSGA to illustrate both positive and negative impacts on the earnings profileKey assumptionsKey assumptions in the model include the following - A GBM process is used to simulate all of the commodity price paths - Correlations are predefined and fixed resulting in a static set of shocks used in the simulation process- User defined volumes and revenues are static- User defined prices and volatiliities are used as baseline assumptions for each quarter

Interface

Interface

REF
1

Cmdy Price Proj

Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
124418764137
1367693617635
2648580611483
786217932233
803244438443
5605736599794
124418764137
4238042982159
1367693617635
1589462370676
2648580611483
803244438443
786217932233
0
803244438443

Correlations

5
95
Mean
Sugar
01107087317
03452715728
02
00854552129
04050827214
021
00695646039
04616729635
022
0061034605
05340468686
023

Sales Pricing Volumes

Sugar
Natural gas
Electricity
Diesel
Aluminum

Gross Exposure

REF
EPS-at-risk
1

Risk Mgmt Portfolio

REF
EBITDA
1

Simulation Net Exposure

Diesel
Electricity
Natural gas
Crude oil
Price change
EPS impact
Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
1912572265773
1138117311849
1319658025171
11406289411
1162397994146
3734236225276
1912572265773
2596118913427
1138117311849
1276460888256
1319658025171
1162397994146
11406289411
0
1162397994146
5
95
Mean
Natural Gas
40688610903
79154680028
57310928297
32742030186
84855775584
57310928297
28887025303
92658501718
57310928297
26679947564
10281600624
57310928297

Net Exposure

5
95
Mean
Electricity
344141498094
1003995388957
622705433902
264877650618
1259146360173
622705433902
219723962528
1384358671012
6227
15528763279
1491896493046
622705433902

Fin Statement

5
95
Mean
Diesel
16508189205
29538139317
22444582473
14672697038
31955148416
22444582473
13074767002
36018057848
22444582473
11953891033
37258114813
22444582473

Price shock contuity

5
95
Mean
Aluminum
134769028369
254013105136
1878
119721652401
284279599425
187771063122
106347176349
316018108859
187771063122
95839609124
346925945325
187771063122

Graph support

Sugar
Natural gas
Electricity
Diesel
Aluminum
128
96
1
16
16
134
101
1
168
17
141
106
1
176
18
150
111
1
185
19
95ile
5ile
Earnings statement
-161353539101
1326569803377
611516334878
-421367106558
1798147036403
760774397443
-642177203637
2144399264928
918591594906
-1050874848209
2521161989213
1080153466317
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Correlations among commodity prices dictate the way in which commodity prices move together and are used to develop commodity price projections via simulation
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Commodity volumes may be determined by reviewing sales and demand planning forecasts Volumes may be aggregated when sourced from different business divisions or regions to arrive at a corporate-wide volume estimate
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Oliver Wyman
File Attachment
AFP-OW Commodity Risk Calculatorxls

step 2 sAles pRiCing And puRChAsing volumes

CoRe AnAlysis user-defined commodity volumes for the predefined set of commodities are coupled

with the distribution of price outcomes to support the determination of gross and net

exposures to commodities sales volumes and prices are incorporated into the pro forma

financial statement to understand earnings impacts from commodity exposures

outputs A predefined set of commodity volumes reflecting commodity demand over time

beneFits bull Centralizes commodity requirements across business units and geographies

bull enables testing of alternative commodity purchase requirements using price

elasticity analysis

in the second step the management team must

estimate the volume of future commodity purchases

across the enterprise the estimated volumes for inputs

are going to be directly linked to sales forecasts and

thus may be derived from product demand profiles

While the current model focuses on price uncertainty

it is important to recognize the presence of volume

uncertainty as well the static volumes are estimated

and then entered by the user on the tab labeled

ldquointerfacerdquo under the heading ldquoCommodity volumesrdquo

Assumptions for product sales volumes and prices are

also entered on the ldquointerfacerdquo tab for later use in the

holistic commodity risk profile assessment

step 3 gRoss exposuRe

CoRe AnAlysis user-defined baseline prices are combined with simulated price paths and volumes to

generate commodity exposure as measured by the difference between the 95th percentile

worst case outcome and the baseline expected outcome

outputs A firmrsquos gross commodity exposure given commodity price and volume

beneFits bull gives context of expected commodity exposure in comparison to pampl and other risks

bull shows changes over time due to either changes in business mix or changes in

commodity price expectations

in the third step the expected gross commodity

exposure is determined the 95th percentile worst case

commodity costs located on the tab labeled ldquogross

exposurerdquo are found by multiplying each commodity

volume by price gross exposure is determined by

summing the difference between the baseline cost and

the simulated 95th percentile worst case cost for each

individual commodity exposure Alternative percentiles

may be selected using the ldquoexposure risk levelrdquo input

value on the ldquointerfacerdquo tab

Copyright copy 2012 oliver Wyman 6

step 4 Risk mAnAgement poRtFolio

CoRe AnAlysis Risk management strategies currently in place are identified

outputs portfolio of existing risk management options (eg a set of commodity derivative hedges)

beneFits bull brings together and coordinates different functions of the organization (ie strategy

sales and marketing procurement treasury) and geographies

bull sets common understanding of risk management options for key commodity exposures

After determining gross commodity exposure the next

step in the process is to identify existing risk management

strategies the tab labeled ldquoRisk mgmt portfoliordquo has

a predefined list of risk management options available

which limit the management decision to financial

hedges that are assumed to lock-in the commodity at the

baseline expected price on the tab labeled ldquointerfacerdquo

under the heading ldquohedge selectionrdquo the user selects

ldquoyesrdquo or ldquonordquo for each commodity depending on whether

the firm plans to hedge that particular commodity it

is important to note that management of commodity

price risk is not limited only to financial hedging rather

it typically involves a combination of four broad levers

ndash each (or a combination thereof) may be effective as

illustrated in exhibit 3

besides recognizing current risk management

strategies this step also alerts management of other

potential risk mitigation options that may be available to

manage key commodities exposures

Copyright copy 2012 oliver Wyman 7

exhibit 3 Risk mAnAgement options

a Product Pricing (incl ldquoPass-throughrdquo)

b Procurement contracts

c Financial hedging

d Value chain integration long-term strategies

oVerView bull use market pricing

power to recover

change in input costs ndash

revenue management

bull balancesharetransfer

risk on purchases ndash

cost management

bull transfer risk to third

party through markets

structured deals

(Reinsurers also providing

capacity here)

bull up or downstream

integration eg

sourcing raw materials

retail trading

examPle strategies

bull employ cost pass-through

mechanisms (across

all or a portion of the

product portfolio)

bull Align price risk transfer

mechanisms with sales and

purchase contracts

bull include contract

mechanisms that mitigate

risk (eg price ceilings

floors price bands knock-

inknock-out clauses)

bull hedge price exposure

with swaps to set the price

or options or collars to

limit downside

bull Cross-hedge exposure

with contracts with

strong correlation to

underlying exposure

bull Acquire upstream company

to ensure supply and

diversify risk exposure

bull identify drivers that

contribute greatest

percentage to overall risk

Pros bull Raw materials costs fully

or partially passed through

to customers

bull stabilized margins and

earnings certainty

bull protection against price

moves and volatility

bull transition risk back

to supplier under

certain structures

bull protection against

feed stock price moves

and volatility

bull no impact on product

pricingsupplier contracts

bull holistic approach to cost

and margin management

bull diversification of

risk exposure

cons bull Customers may substitute

products or move to

lower priced competitor

brands in response to

product price

bull suppliers demand

premium (higher price)

for risk sharing

bull need to find

receptive suppliers

bull liquidity premium and

market non-availability

for many commodities

bull basis risk from

cross-hedging

bull Requires appropriate

capital and expertise

for joint ventures

or acquisitions

bull increased organizational

complexity

Copyright copy 2012 oliver Wyman 8

step 5 net exposuRe

CoRe AnAlysis predefined financial hedges and price correlations between commodities are applied to

gross commodity exposure to determine net commodity exposure for the enterprise

outputs A firmrsquos net commodity exposure after incorporating its current risk management portfolio

beneFits bull Accounts for natural hedges

bull provides management with an understanding of the effectiveness of the risk

management portfolio vs desired exposure

bull provides broad understanding of how commodity price risk flows through

the organization

While calculating net exposure management must also

take into account the correlation between commodity

prices price movements of one commodity may be related

to the price movement of other commodities thereby

increasing or decreasing exposure depending on the

relative strength of the relationship to account for this the

net exposure of the portfolio must be determined on the

tab labeled ldquosimulation net exposurerdquo the distribution

of terminal price values for each commodity and time

period (eg 1st quarter 2nd quarter etc) are multiplied

against volume for that same time period A distribution

of net exposures is generated and 5th and 95th percentile

outcomes for the portfolio series are selected

A companyrsquos net exposure to commodity price volatility is

shown on the tab labeled ldquonet exposurerdquo the predefined

volume levels are multiplied by the new correlated prices

to determine the 95th percentile worst case net exposure

if the company is employing a hedging strategy for a

commodity then it has no exposure for that particular

commodity since hedges are assumed to lock-in the

commodity at the baseline expected price

step 6 holistiC Commodity Risk pRoFile

CoRe AnAlysis Commodity exposures and price projections are combined with predefined estimates for

revenues and other fixed costssgA to illustrate both positive and negative impacts on the

earnings profile

outputs pro forma financial statement with predefined revenue and fixed costsgA reflecting the

impact of price variations and hedging on earnings

beneFits bull enables objective evaluation and comparison of a wide variety of risk

management strategies

bull promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at an

individual bu level

bull gives management a view of earnings impact from commodity prices at different levels

of probability

the process of determining net commodity exposure

ends with the creation of a holistic company-wide

risk profile ndash with the sensitivities in price projections

identified in step 1 used to explore the potential impact

on ebitdA debt covenants and other financial metrics

this impact is shown on the tab labeled lsquointerfacersquo in the

section called ldquoholistic commodity profile and impact

on earningsrdquo

Copyright copy 2012 oliver Wyman 9

ConClusion

large and sustained commodity price swings are quickly reshaping industries and the

businesses that exist within them in this environment commodity risk management must

be firmly integrated into the strategy of the company and structured under a commodity risk

management framework

executives need to recognize that their strategies may have to change to accommodate how

volatile commodity prices are redefining their competitive landscape and develop a process

to manage them across functions decisions around commodity risk management must be

based on a forward-looking view this process can include a series of tough questions about

the sustainability of business models such as

bull Will a perception of scarcity resulting from sustained volatile commodity prices impact

that commodityrsquos availability

bull Will changing commodity correlations invalidate diversification plays to hedge against

other commodities

bull Will consumers shift to cheaper private label alternatives or change their lifestyles to

avoid expenses introduced by higher commodity prices

the companies who are first to define their risk management metrics to include extreme

prices and anticipate how they can best benefit from them will have a significant

competitive advantage

Copyright copy 2012 oliver Wyman 10

Copyright copy 2012 oliver Wyman 11

For more information please contact

michael denton Partner global risk and trading oliver wyman

+16463648440 michaeldentonoliverwymancom

michael denton is a new york-based partner in oliver Wymanrsquos global Risk amp trading practice with specialized experience in energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market risk modelling portfolio dynamics and risk-based decision frameworks

alex wittenberg Partner global risk and trading oliver wyman

+16463648440 alexwittenbergoliverwymancom

Alex Wittenberg has over 20 years of cross-industry experience in risk management advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision making and financial performance designing risk governance for boards and management and developing corporate risk monitoring mitigation and transfer frameworks

brian t Kalish director Finance Practice lead

+13019616564 bkalishafponlineorg

brian t kalish is the director and Finance practice lead of the Association of Financial professionals he is responsible for the Finance practice which includes Corporate Finance Risk management Capital markets investor Relations Financial planning amp Analysis Accounting and Financial Reporting

oliveR WymAn And the AssoCiAtion FoR FinAnCiAl pRoFessionAls the FinAnCiAl insights seRies

this is part of series of papers by international management consulting firm oliver Wyman and the Association for Financial professionals that address critical issues for finance professionals oliver Wyman and AFp welcome your thoughts on the ideas presented in this paper if you would like to provide feedback or be alerted when we schedule events or other activities for corporate finance professionals please contact one of the individuals listed below

oliver Wyman is an international management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development

the Association for Financial professionals (AFp) headquartered outside Washington dC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFp is the daily resource for the finance profession

wwwoliverwymancom

Copyright copy oliver Wyman All rights reserved

AuthoRs

michael denton Partner global risk and trading oliver wyman

alex wittenberg Partner global risk and trading oliver wyman

brian t Kalish director Finance Practice lead

Gross Exposure graph support (stacked graph and waterfall)
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 5606 4238 1589 803 00
Grey 1244 1368 2649 786 803
Sugar 1244 186 261 341 456 1244
Natural gas 1368 210 278 375 505 1368
Electricity 2649 381 636 762 869 2649
Diesel 786 113 160 239 274 786
Aluminum 803 106 164 231 302 803
Commodities volume graph support
Q1 Q2 Q3 Q4
Sugar 12800 13400 14100 15000
Natural gas 960 1010 1060 1110
Electricity 100 100 100 100
Diesel 1600 1680 1760 1850
Aluminum 160 170 180 190
Net exposure graph support
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 3734 2596 1276 1162 00
Grey 1913 1138 1320 114 1162
Sugar 1913 207 404 530 771 1913
Natural gas 1138 168 250 348 372 1138
Electricity 1320 221 266 331 502 1320
Diesel 114 26 39 12 38 114
Aluminum 1162 151 223 340 448 1162
Financial statement graph support
Cost (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 463 686 840 1116 3105
Volume (lbs) 1280 1340 1410 1500 463 686 840 1116 3105 Unhedged
Price 036 051 060 074
Natural gas 718 829 956 1008 718 829 956 1008 3511
Volume 96 101 106 111 718 829 956 1008 3511 Unhedged
Price 75 82 90 91
Electricity 844 888 953 1125 844 888 953 1125 3810
Volume 10 10 10 10 844 888 953 1125 3810 Unhedged
Price 844 888 953 1125
Diesel 385 416 407 453 385 416 407 453 1660
Volume 160 168 176 185 385 416 407 453 1660 Unhedged
Price 24 25 23 24
Aluminum 451 543 678 805 451 543 678 805 2477
Volume 16 17 18 19 451 543 678 805 2476841735006 Unhedged
Price 282 319 377 424
Total 2861 3361 3834 4507 2861 3361 3834 4507 14564
2861 3361 3834 4507 14563772697504 Unhedged
Cost (5)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 179 142 141 119 179 142 141 119 581
Volume (lbs) 1280 1340 1410 1500 179 142 141 119 581 Unhedged
Price 014 011 010 008
Natural gas 347 283 251 241 347 283 251 241 1123
Volume 96 101 106 111 347 283 251 241 1123 Unhedged
Price 36 28 24 22
Electricity 332 223 176 138 332 223 176 138 868
Volume 10 10 10 10 332 223 176 138 868 Unhedged
Price 332 223 176 138
Diesel 275 264 244 231 275 264 244 231 1015
Volume 160 168 176 185 275 264 244 231 1015 Unhedged
Price 17 16 14 12
Aluminum 241 230 236 205 241 230 236 205 911
Volume 16 17 18 19 241 230 236 205 911127595797 Unhedged
Price 150 135 131 108
Total 1373 1142 1048 935 1373 1142 1048 935 4498
95ile 5ile
Sales 480 504 529 556 480 504 529 556
Fixed Costs 120 120 120 120 120 120 120 120
Variable Costs 286 336 383 451 137 114 105 93
General amp Admin Exp 90 90 90 90 90 90 90 90
EBITDA (16) (42) (64) (105) 133 180 214 252
Title To quickly check to make sure prices are still the same filter the prices on the tab proj 3 to go in order 1 to 100 and this should match
[Optional comments]
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
Earnings statement
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015
000
Cost of Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($lbs) 2560 2814 3102 3450 11926
Volume (lbs) 12800 13400 14100 15000
Price 020 021 022 023
000
Natural gas ($Kcf) - Henry Hub 5502 5788 6075 6362 23727
Volume 960 1010 1060 1110
Price 573 573 573 573
000
Electricity ($MWh) - NY ISO 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($gal) - NY Harbour 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
000
General amp Admin Exp 9000 9000 9000 9000 36000
000
EBITDA 6115 7608 9186 10802 33710
Impact of hedging
all figures in millions (except prices)
Futures Used
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
Net exposure (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 207 404 530 771 1913
Volume (lbs) 1280 1340 1410 1500
Price 036 051 060 074
00 00 00 00 00
Natural gas 718 829 956 1008 168 250 348 372 1138
Volume 96 101 106 111
Price 75 82 90 91
00 00 00 00 00
Electricity 844 888 953 1125 221 266 331 502 1320
Volume 10 10 10 10
Price 844 888 953 1125
00 00 00 00 00
Diesel 385 416 407 453 26 39 12 38 114
Volume 160 168 176 185
Price 24 25 23 24
00 00 00 00 00
Aluminum 451 543 678 805 151 223 340 448 1162
Volume 16 17 18 19
Price 282 319 377 424
Total 2861 3361 3834 4507 773 1182 1561 2131 5647
Correlated Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum Total Net Exposure
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Full-Year
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Volume 12800 13400 14100 15000 960 1010 1060 1110 100 100 100 100 1600 1680 1760 1850 160 170 180 190
Simulated
5 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 48715
95 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145722
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
Mean
Net Exposure Net Portfolio Exposure
32380 47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739 32380
33948 42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789 33948
39948 55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685 39948
43542 60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103 43542
44977 68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 44977
48911 50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956 48911
49824 97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168 49824
52614 9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173 52614
54247 89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834 54247
54467 29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518 54467
56081 37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959 56081
56950 66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358 56950
58740 44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783 58740
59233 63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890 59233
60239 11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751 60239
60316 3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213 60316
61664 56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573 61664
65269 4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321 65269
65273 92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204 65273
65764 69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759 65764
65876 93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008 65876
66078 87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886 66078
66116 74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632 66116
66562 28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686 66562
66781 5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205 66781
68244 27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291 68244
69290 94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559 69290
69701 32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209 69701
70796 10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158 70796
71222 62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991 71222
71261 45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691 71261
72702 91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228 72702
73734 20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550 73734
73801 52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806 73801
74108 88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414 74108
74317 1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024 74317
74660 22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160 74660
74808 31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368 74808
74998 82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171 74998
75350 8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146 75350
75850 85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158 75850
76270 2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409 76270
78037 30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375 78037
78226 23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103 78226
81022 96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662 81022
82068 99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748 82068
82643 35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858 82643
87060 14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447 87060
87614 83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068 87614
89614 46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928 89614
90297 100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267 90297
91492 49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762 91492
92285 79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150 92285
92517 80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079 92517
92520 58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466 92520
93297 38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511 93297
93492 84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782 93492
94010 72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702 94010
95355 25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982 95355
95981 51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310 95981
96590 70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430 96590
96659 59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987 96659
98984 39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335 98984
99473 64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881 99473
100438 18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643 100438
104925 26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805 104925
106683 71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274 106683
107046 48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657 107046
108929 75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922 108929
109789 17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544 109789
109940 98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393 109940
111963 61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121 111963
113140 41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558 113140
114475 15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927 114475
115420 76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479 115420
115470 12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839 115470
115543 43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938 115543
115599 16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233 115599
115628 95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056 115628
115695 13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336 115695
116747 78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058 116747
118794 33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808 118794
120798 67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960 120798
122134 7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729 122134
123516 73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263 123516
125606 90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639 125606
126928 54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417 126928
128846 77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264 128846
128878 6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513 128878
130807 34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239 130807
140398 19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517 140398
142414 81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603 142414
143917 53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292 143917
145005 65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126 145005
145638 36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145638
147330 40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883 147330
149955 86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437 149955
167385 24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467 167385
178469 57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836 178469
195830 21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430 195830
Risk management portfolio
Futures Description
Sugar Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Natural gas Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Electricity Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Diesel Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Aluminum Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Gross exposure calculations
Gross Exposure
all figures in millions (except prices) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 442 543 651 801 186 261 341 456 1244
Volume (lbs) 1280 1340 1410 1500
Price 035 041 046 053
00 00 00 00 00
Natural gas 760 857 982 1141 210 278 375 505 1368
Volume (MMBTU) 96 101 106 111
Price 792 849 927 1028
00 00 00 00 00
Electricity 1004 1259 1384 1492 381 636 762 869 2649
Volume (MWh) 10 10 10 10
Price 1004 1259 1384 1492
00 00 00 00 00
Diesel 473 537 634 689 113 160 239 274 786
Volume (gal) 160 168 176 185
Price 295 320 360 373
00 00 00 00 00
Aluminum 406 483 569 659 106 164 231 302 803
Volume (tons) 16 17 18 19
Price 2540 2843 3160 3469
Total 3085 3679 4220 4783 996 1500 1947 2407 6850
Sales Pricing and Volumes
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015 016
000
Cost of Commodity Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($) 2560 2814 3102 3450 11926
Volume (lb) 12800 13400 14100 15000
($lb) 020 021 022 023
000
Natural gas ($) 5502 5788 6075 6362 23727
Volume (MMBTU) 960 1010 1060 1110
Price ($MMBTU) 573 573 573 573
000
Electricity ($) 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($l) 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum ($) 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
Correlations
Correlated random shocks generated from correlation matrix
Correlations Sugar Natural gas Electricity Diesel Aluminum
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Sugar Q1 100 099 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q2 099 100 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q3 099 099 100 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q4 099 099 099 100 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Nat Gas Q1 065 065 065 065 100 099 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q2 065 065 065 065 099 100 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q3 065 065 065 065 099 099 100 099 094 094 094 094 086 086 086 086 069 069 069 069
Q4 065 065 065 065 099 099 099 100 094 094 094 094 086 086 086 086 069 069 069 069
Electricity Q1 055 055 055 055 094 094 094 094 100 099 099 099 082 082 082 082 071 071 071 071
Q2 055 055 055 055 094 094 094 094 099 100 099 099 082 082 082 082 071 071 071 071
Q3 055 055 055 055 094 094 094 094 099 099 100 099 082 082 082 082 071 071 071 071
Q4 055 055 055 055 094 094 094 094 099 099 099 100 082 082 082 082 071 071 071 071
Diesel Q1 047 047 047 047 086 086 086 086 082 082 082 082 100 099 099 099 066 066 066 066
Q2 047 047 047 047 086 086 086 086 082 082 082 082 099 100 099 099 066 066 066 066
Q3 047 047 047 047 086 086 086 086 082 082 082 082 099 099 100 099 066 066 066 066
Q4 047 047 047 047 086 086 086 086 082 082 082 082 099 099 099 100 066 066 066 066
Aluminum Q1 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 100 099 099 099
Q2 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 100 099 099
Q3 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 100 099
Q4 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 099 100
Column C C C C G G G G - 0 K K K - 0 O O O S S S S
Indirect C_Prices - 0 - 0
Correlated Sugar Natural gas Electricity Diesel Aluminum
N(01) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1 (108) (092) (082) (107) (047) (078) (057) (062) (015) (003) (008) (027) 041 052 041 022 052 051 036 039
2 031 (007) (006) (003) (015) (014) (012) (009) (033) (017) (021) (009) 005 005 026 (009) (087) (103) (079) (074)
3 (176) (167) (159) (155) (092) (079) (090) (098) (058) (061) (057) (069) (009) 018 007 002 (124) (101) (128) (121)
4 (134) (119) (112) (122) (046) (044) (043) (052) (066) (052) (037) (044) 016 009 (016) (003) (106) (116) (111) (094)
5 (075) (080) (049) (077) (063) (028) (057) (072) (045) (056) (051) (042) 011 010 021 032 (135) (127) (130) (123)
6 082 082 072 095 137 110 115 132 119 098 119 143 095 104 085 108 092 107 098 107
7 035 050 038 044 098 108 105 100 140 129 127 131 123 119 119 139 (024) (022) (013) (010)
8 (056) (044) (076) (066) (017) (033) (033) (045) (009) (007) 003 016 (091) (074) (102) (071) 078 068 080 058
9 (025) (015) (023) (027) (104) (127) (109) (105) (135) (150) (144) (166) (149) (148) (165) (151) (130) (126) (134) (131)
10 126 116 115 112 (120) (097) (107) (114) (054) (046) (052) (053) (107) (099) (125) (099) (117) (120) (125) (135)
11 (166) (182) (173) (174) (090) (083) (095) (089) (061) (070) (065) (060) (066) (048) (061) (084) 006 (013) (002) (006)
12 014 014 034 034 074 062 073 046 131 148 164 155 003 (003) 002 (035) 006 (003) 000 010
13 034 028 011 035 142 130 113 120 118 112 086 091 060 030 044 052 085 081 088 084
14 (048) (059) (063) (068) 033 026 020 050 021 017 019 023 001 (012) 009 005 089 097 075 099
15 079 091 090 091 083 082 073 081 096 086 093 081 107 112 115 096 047 038 030 024
16 058 071 034 059 116 117 122 103 064 084 084 092 081 080 068 108 093 092 082 070
17 (046) (041) (043) (048) 099 112 121 101 138 111 102 126 031 049 027 052 (019) (030) (033) (045)
18 030 052 025 024 060 078 068 059 073 093 069 070 031 033 026 030 (020) (037) (024) (026)
19 (034) (031) (025) (026) 146 114 124 151 166 173 175 161 121 116 108 097 222 227 201 212
20 (119) (138) (136) (114) (009) (009) 003 (012) (048) (021) (028) (040) 073 048 037 039 (028) (009) (003) (044)
21 092 081 085 085 236 228 257 241 229 231 230 239 259 264 269 263 098 082 075 097
22 047 052 031 042 (014) (030) (006) (016) (028) (027) (041) (031) (035) (040) (014) (031) (122) (127) (135) (134)
23 (043) (047) (046) (069) (021) (019) (014) (044) (005) 020 005 (006) (032) (025) (021) (012) 075 060 052 051
24 082 083 064 089 195 192 202 195 198 209 195 198 138 109 128 113 201 194 206 208
25 039 030 035 039 042 037 048 054 067 069 066 059 (050) (065) (051) (061) 032 038 040 033
26 130 157 113 138 057 052 036 048 076 085 063 076 (053) (058) (061) (052) 009 004 (012) 004
27 (063) (063) (067) (046) (041) (045) (053) (035) 012 015 004 (003) (140) (136) (152) (120) (116) (127) (101) (101)
28 (051) (067) (068) (058) (075) (063) (051) (068) (056) (050) (053) (068) (031) (025) (019) (053) (022) (005) (014) (018)
29 (165) (163) (176) (155) (115) (130) (143) (128) (065) (067) (081) (085) (128) (105) (126) (127) (058) (047) (031) (050)
30 (029) (031) (029) (032) (007) (016) (019) (003) 010 018 005 012 001 017 007 (007) (077) (089) (071) (081)
31 (040) (055) (058) (043) (004) (009) (010) (041) (007) (023) (027) (028) (065) (082) (086) (075) 078 068 065 089
32 123 098 102 105 (047) (059) (069) (079) (101) (106) (094) (097) (069) (074) (072) (089) (125) (131) (115) (122)
33 057 052 066 070 082 074 077 075 085 082 081 079 079 090 068 091 245 239 238 237
34 174 156 162 156 142 131 136 135 081 071 075 086 143 133 150 126 046 052 053 071
35 (121) (127) (111) (122) (004) (005) 018 (009) 051 059 046 032 (021) 003 (018) (008) 034 046 012 013
36 187 205 195 203 123 123 130 120 105 097 101 120 046 051 025 042 262 246 265 270
37 (019) (031) (029) (029) (106) (097) (106) (108) (103) (098) (084) (097) (090) (092) (107) (106) (199) (187) (190) (194)
38 080 080 081 069 018 036 (001) 014 (002) (022) (018) (017) 074 071 073 069 109 102 092 107
39 040 053 044 058 085 065 070 063 020 040 029 023 029 053 042 047 076 059 077 084
40 085 100 108 089 134 123 111 133 158 169 164 168 151 138 173 169 149 174 152 150
41 082 097 061 076 101 085 105 108 092 079 093 076 111 111 112 113 (045) (064) (044) (042)
42 (123) (134) (135) (147) (263) (271) (274) (254) (270) (276) (279) (268) (210) (223) (208) (227) (245) (244) (236) (255)
43 (016) (018) (018) (034) 100 092 094 115 122 134 110 112 115 109 102 111 027 024 035 026
44 (048) (052) (042) (035) (120) (122) (121) (119) (085) (098) (098) (095) (112) (115) (120) (132) (006) 005 001 (000)
45 069 067 059 054 (073) (073) (070) (067) (057) (073) (070) (080) (033) (026) (039) (057) (045) (040) (018) (017)
46 005 011 013 005 014 048 036 037 031 036 041 047 (043) (018) (052) (039) 030 037 050 024
47 (235) (256) (232) (222) (247) (231) (242) (221) (232) (219) (219) (235) (272) (277) (282) (278) (312) (301) (287) (275)
48 143 131 119 126 084 106 097 108 (004) 017 025 021 087 072 067 071 (004) 001 (000) (023)
49 (063) (045) (054) (035) 023 021 028 027 075 105 080 081 (048) (052) (049) (045) (006) 001 002 (004)
50 (055) (060) (066) (079) (129) (132) (138) (130) (154) (145) (127) (155) (157) (121) (133) (121) (205) (205) (217) (195)
51 039 028 044 031 052 056 043 052 030 025 046 023 035 034 022 042 079 079 067 081
52 (087) (075) (088) (067) (009) 002 (016) (002) 007 (011) (013) 000 (087) (077) (079) (081) (001) 016 (007) 004
53 (032) (024) (025) (035) 149 156 161 156 156 170 148 179 148 147 170 141 170 188 173 191
54 186 179 168 155 124 117 143 121 053 066 061 058 106 101 095 112 093 084 097 095
55 (135) (144) (137) (150) (190) (192) (201) (188) (170) (167) (161) (174) (185) (199) (201) (171) (296) (281) (273) (299)
56 (052) (051) (058) (040) (075) (083) (084) (087) (111) (124) (115) (109) (019) (009) 002 (019) (046) (047) (058) (039)
57 173 174 186 206 185 163 174 165 179 199 202 204 095 113 095 113 240 264 251 239
58 076 087 072 084 039 077 056 072 (005) 026 004 024 008 (004) (018) (002) (056) (058) (067) (061)
59 (072) (037) (056) (072) 059 073 063 057 048 051 060 051 074 102 100 080 033 037 022 034
60 (109) (093) (104) (106) (170) (175) (156) (156) (192) (185) (165) (178) (238) (234) (237) (243) (122) (127) (109) (150)
61 044 048 032 047 104 106 096 106 063 077 086 081 102 092 089 080 079 079 059 054
62 (105) (101) (086) (094) 009 (006) (009) (007) (027) (025) (023) (025) 026 015 030 041 (161) (157) (166) (184)
63 (095) (103) (105) (122) (077) (072) (095) (081) (123) (108) (120) (115) (077) (075) (081) (075) 020 018 028 018
64 (079) (070) (083) (086) 074 070 060 069 062 065 077 059 121 103 119 133 016 011 (005) 017
65 096 093 106 097 146 131 129 142 147 140 150 146 169 166 156 154 176 197 186 175
66 (049) (046) (037) (042) (084) (088) (081) (084) (110) (125) (112) (120) (149) (140) (151) (124) (042) (051) (064) (085)
67 061 045 052 064 096 126 115 098 107 100 105 103 157 142 150 155 009 034 031 029
68 (085) (114) (100) (117) (180) (194) (192) (178) (165) (186) (182) (184) (135) (123) (134) (140) (131) (134) (116) (157)
69 (084) (062) (057) (060) (071) (050) (052) (053) (104) (110) (109) (100) 028 002 031 008 (003) (012) (008) (004)
70 (002) 011 007 002 058 065 058 057 042 027 035 034 069 050 045 037 066 076 106 097
71 023 024 035 027 075 066 071 081 095 084 089 077 039 040 032 039 076 096 076 076
72 (005) (026) (008) (013) 061 048 062 036 027 026 034 017 121 143 116 129 (056) (025) (024) (015)
73 048 050 056 053 118 111 110 109 097 090 099 083 203 196 178 200 072 102 084 074
74 (119) (122) (110) (137) (040) (041) (045) (036) (033) (031) (018) (030) (071) (076) (086) (093) (044) (057) (050) (028)
75 114 095 103 110 098 087 111 092 026 041 026 023 109 106 108 120 027 029 017 023
76 057 048 022 022 085 080 076 090 086 072 090 077 167 169 163 169 098 084 086 103
77 190 171 152 155 112 132 124 105 111 085 105 108 040 053 065 059 046 074 069 075
78 077 059 048 077 080 079 090 080 111 120 103 119 089 077 070 073 059 064 042 045
79 118 100 116 107 005 (002) 006 011 010 031 016 013 (033) (034) (056) (059) 046 050 062 058
80 033 017 045 032 047 053 045 050 (010) 004 (012) (001) 066 093 082 077 045 034 044 048
81 182 197 190 205 085 101 107 099 114 122 103 125 081 073 071 071 197 192 209 220
82 076 081 062 078 (080) (086) (091) (084) (093) (085) (087) (086) (006) (014) (000) 009 082 069 074 061
83 156 153 147 166 006 (004) 020 003 (019) (018) (026) (025) (052) (052) (037) (048) (178) (171) (159) (160)
84 017 (010) (000) 003 048 058 066 040 042 051 037 048 020 052 037 030 006 006 (001) (000)
85 006 007 006 (000) 009 (020) (017) (013) 010 005 007 023 (045) (064) (063) (069) (131) (148) (138) (135)
86 078 080 076 077 162 150 131 145 144 154 144 151 281 273 262 269 091 085 107 097
87 (034) (019) (010) (011) (080) (060) (085) (074) (086) (087) (093) (097) (024) (034) (033) (036) (001) 001 010 017
88 (070) (046) (064) (058) (028) (040) (049) (046) (009) 005 (021) (021) 038 056 036 033 (045) (052) (050) (073)
89 (031) (037) (025) (013) (099) (097) (094) (098) (097) (107) (096) (084) (158) (167) (144) (163) (221) (230) (232) (238)
90 123 130 136 128 057 060 088 099 103 109 105 089 112 116 115 112 133 118 133 122
91 (146) (126) (133) (132) (037) (026) (029) (035) (010) (020) (038) (029) 000 (024) (020) (022) 059 068 077 069
92 (001) 007 (009) 018 (073) (053) (058) (058) (065) (078) (068) (070) (048) (052) (051) (052) (112) (143) (127) (123)
93 (226) (202) (225) (216) (056) (079) (073) (055) (020) (046) (045) (052) (011) (017) (014) (016) 027 021 039 037
94 (131) (113) (112) (115) (025) (019) (023) (024) (021) (013) (037) (047) (034) (005) (048) (023) (037) (042) (041) (042)
95 (087) (084) (091) (090) 066 068 041 072 116 108 119 121 152 144 136 136 152 180 159 168
96 (042) (035) (040) (038) (018) (001) (010) (018) 011 004 025 008 031 040 035 039 (011) (013) (012) (022)
97 (151) (157) (167) (162) (122) (147) (143) (134) (141) (132) (114) (134) (093) (074) (093) (093) (108) (119) (109) (132)
98 096 101 091 109 097 102 101 107 070 074 071 040 097 098 089 099 (062) (089) (089) (077)
99 033 028 029 021 (008) (017) (022) (009) (001) (010) 018 (002) 027 008 007 033 (038) (031) (030) (006)
100 (055) (051) (066) (067) 011 011 029 005 018 022 034 038 091 085 091 088 073 071 087 075
Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Simulated
5 011 009 007 006 407 327 289 267 3441 2649 2197 1553 165 147 131 120 1348 1197 1063 958
95 035 041 046 053 792 849 927 1028 10040 12591 13844 14919 295 320 360 373 2540 2843 3160 3469
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024
2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409
3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213
4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321
5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205
6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513
7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729
8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146
9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173
10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158
11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751
12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839
13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336
14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447
15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927
16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233
17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544
18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643
19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517
20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550
21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430
22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160
23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103
24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467
25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982
26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805
27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291
28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686
29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518
30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375
31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368
32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209
33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808
34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239
35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858
36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238
37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959
38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511
39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335
40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883
41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558
42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789
43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938
44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783
45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691
46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928
47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739
48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657
49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762
50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956
51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310
52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806
53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292
54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417
55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685
56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573
57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836
58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466
59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987
60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103
61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121
62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991
63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890
64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881
65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126
66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358
67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960
68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081
69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759
70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430
71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274
72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702
73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263
74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632
75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922
76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479
77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264
78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058
79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150
80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079
81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603
82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171
83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068
84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782
85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158
86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437
87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886
88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414
89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834
90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639
91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228
92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204
93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008
94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559
95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056
96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662
97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168
98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393
99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748
100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1
1
185947613236 209700016612 381289955056 113496909508 105940968217
261410846662 278202957602 636440926272 159777507841 164064511715
340758878594 374684278258 761658671012 238893166595 230844682326
456070302878 505106365162 869191059145 27405034829 302394276185
1 1 1
2 2 2
3 3 3
4 4 4
2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure
Sugar Sugar Sugar Sugar Sugar Sugar Sugar
Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas
Electricity Electricity Electricity Electricity Electricity Electricity Electricity
Diesel Diesel Diesel Diesel Diesel Diesel Diesel
Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum
1
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
USER INPUTS RISK PROFILE OUTPUTS
Date 1112 5 Holistic commodity profile and impact on earnings (baseline 95 confidence band 5 confidence band)
Price and Volatility Base 5th -ile 95th -ile
Q1 Q2 Q3 Q4 2012 Un hedged Hedged Impact Un hedged Hedged Impact
Price Volatility Price Volatlity Price Volatility Price Volatility of hedge of hedge
Sugar ($lbs) $ 020 70 $ 021 70 $ 022 70 $ 023 70 Sales 2069 2069 2069 - 0 2069 2069 - 0
Natural gas ($MMBTU) $ 573 48 $ 573 48 $ 573 48 $ 573 48 Cost of Goods Sold
Electricity ($MWh) $ 6227 69 $ 6227 69 $ 6227 69 $ 6227 69 Fixed Costs 480 480 480 - 0 480 480 - 0
Diesel ($gal) $ 224 37 $ 224 37 $ 224 37 $ 224 37 Sugar 119 58 58 - 0 311 311 - 0
Aluminum ($KTon) $ 1878 32 $ 1878 32 $ 1878 32 $ 1878 32 Natural gas 237 112 112 - 0 351 351 - 0
Electricity 249 87 87 - 0 381 381 - 0
Exposure risk level 95 Diesel 155 101 101 - 0 166 166 - 0
Aluminum 131 91 91 - 0 248 248 - 0
Commodity volumes (in millions) Variable Costs 892 450 450 - 0 1456 1456 - 0
Q1 Q2 Q3 Q4 Total COGS 1372 930 930 - 0 1936 1936 - 0
Sugar (lbs) 1280 1340 1410 1500 Gen amp Admin Exp 360 360 360 - 0 360 360 - 0
Natural gas (MMBTU) 96 101 106 111 EBITDA 337 779 779 - 0 (228) (228) - 0
Electricity (MWh) 10 10 10 10
Diesel (gal) 160 168 176 185
Aluminum (KTon) 16 17 18 19 4 Net commodity exposure 2012 by commodity (in millions $)
Other financial statement assumptions
Q1 Q2 Q3 Q4
Sales volume (in millions) 3200 3360 3528 3704
Sales price ($) 015 015 015 015
Fixed costs ($ millions) 120 120 120 120
GampA Exp ($ millions) 90 90 90 90
Hedge Selection
Futures
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
3 Gross commodity exposure 2012 (in millions $)
a) By quarter b) By commodity
All exposures reported as the difference between the 95th percentile highest exposure and the expected baseline gross commodity exposure
2 Estimated commodity volumes for 2012 by quarters (variable units in millions $)
1 Commodity price projections (baseline 95 confidence band 5 confidence band)
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
Read Me - File Overview
Page 2: Commodity Risk Management - Oliver Wyman

About this RepoRt

this report and the associated workbook provide guidance for companies to better

understand and quantify the impact of commodity risks on earnings in order to improve an

organizationrsquos commodity risk management

the supporting excel workbook is available by clicking on the pushpin icon this guide is a

companion piece to ldquoVolatility not Vulnerabilityrdquo published by the Association for Financial

professionals (AFp) and oliver Wyman in october 2011 the article is available by clicking on

the pushpin icon or can be found online at wwwoliverwymancom

CoRpoRAte ChAllenges in mAnAging Commodity Risk

bull developing management strategies (pricing procurement and hedging) using a holistic risk-return perspective instead of just heuristics to make decisions

bull evaluating portfolios of potential mitigation strategies which have impact on internal diversification and aggregation in adequate depth

bull Automating the aggregation and integration of a wide range of data and assumptions (eg purchasing contracts commodity forecasts ability to pass-through)

bull Applying sufficient tools to engage suppliers on contract terms using a risk-return perspective and to develop innovative structures that are mutually beneficial

bull engaging champions and senior sponsors who are passionate about implementing long-term risk mitigation strategies such as revised contract structures or pricing changes

bull treating commodity price risk management as a ldquomargin stabilizationrdquo process rather than a ldquocostrdquo

Global Risk Center

VOLATILITY NOT VULNERABILITYTO SURVIVE SWINGS IN COMMODITY PRICES COMPANIES MUST FIRST BUILD A RISK-MANAGEMENT FRAMEWORK

Historically most businesses could simply withstand changes in commodity prices given that the swings were usually temporary cyclicalmdashand manageable However structural changes in the global economy are creating wilder swings in commodity prices that are not only affecting short-term profits but also long-term planning and investment In this environment every company must develop a formal risk management approach to counter the growing volatility in commodity prices For corporate leaders this means building the infrastructure governance programs and analytical capabilities that can help them better manage their exposure to commodities

1 COMMODITY PRICES AND VOLATILITY

After a brief respite in 2009 the volatility that has come to define many commodity markets

roared back in the latter half of 2010mdashmuch to the dismay of businesses for whom oil

industrial metals and other raw materials comprise a significant share of their costs Crude

oil cracked the psychological barrier of $100 per barrel major food price indexes reached

record highs in the first quarter of 2011 and base and industrial metals such as copper and

aluminum also reached new highs mdashcreating significant pain for buyers (see Exhibit 1) Even

where prices pulled back the cost of many commodities remained higher than beforemdashthe

result of structural shifts in supply and demand on both a local and global scale (For a closer

look at the causes of volatility see Exhibit 2)

These commodity shocks are not only cutting into corporate profits but are testing the

abilities of even the best businesses to plan for and invest in the future Already in 2011 the

CEOs of consumer-facing companies as diverse as PepsiCo Kraft Kimberly-Clark and Levirsquos

have said they expect commodity price inflation to pose a significant challenge to continued

earnings growth over the next several years Those in the middle of the value chain have a

tougher challenge as they cannot easily raise prices to recover lost margins Few expect this

storm to pass quickly The 2011 World Economic Forum Global Risks Survey found corporate

executives in agreement that a key risk they face in the coming decade is extreme volatility in

energy and other commodity prices1

1 World Economic Forum ldquoGlobal Risks 2011rdquo 6th edition January 2011 Oliver Wyman was a contributor to that report

EXHIBIT 1 COMMODITY PRICES ARE SOARING INDEX

8

12

10

6

4

2

0

14

MULTIPLE OF INITIAL PRICEREAL PRICE HISTORY

1987 1991 1995 2003 20111999 2007

Crude Oil

Copper

Wheat

source Datastream 2011

Copyright copy 2011 Oliver Wyman 3

ExhIBIT 2 WhY ThE VOLATILITY

CAUSE DESCRIPTION

Erratic weather Catastrophic weather events have affected production (2009 drought in Australia affected global wheat prices)

Emerging markets Growth in emerging markets has increased demand for food energy and raw materials Global food output will have to rise 70 by 2050 to meet demand while global energy demand is predicted to increase by 36 between 2008-2035

Speculation Emergence of commodities as an investment class (development of commodities-based ETFs)

Infrastructure spending Deteriorating infrastructure in developed countries and a lack of infrastructure in emerging economies hampers the physical flow of commodities

Supply-country strategies Development of market-distorting trade policies (Russian wheat export ban followed crop shortfall in 2010)

Purchasing-country strategies

Countries developing more sophisticated buying capabilities (Korea opened a grain-trading office in Chicago in 2011)

Food and Agriculture Organization of the United Nations World Summit on Food Security November 2009

International Energy Agency World Energy Outlook 2010

This volatility could persist for years particularly given that governments appear willing to disrupt or intervene in markets including halting exports These collective forces have created pressure for corporations to develop strategies to mitigate this growing risk While companies in agri-processing oil refining and wholesale electricity generation have developed formal approaches to trading and risk management programs the majority of companies need to do moremdashmuch more

Given the likelihood of further volatility in commodity prices every company must adopt an analytic risk framework based on a clear understanding of its exposure This paper offers guidance on developing a plan for managing commodity risks that draws on the best practices of companies from around the world The approach is built around the three pillars of a ldquobest practicesrdquo program governance infrastructure and analytics The paper provides an in-depth review of the role of analytics and outlines a new systematic approach to managing commodity risk illustrated through case studies

ONE COMPANYrsquoS MOVES TO TAME ITS VULNERABILITY TO VOLATILITY

A leading industrial company failed to deliver its projected earnings due to a decline in the profitability of non-core energy activities To measure the companyrsquos total exposure to energy volatilitymdashand quantify the potential effect on future profits it needed to develop an integrated energy risk profile In other words the company needed the ability to aggregate the energy exposure of each business unit and evaluate the effectiveness of existing mitigation efforts before it could truly understand net exposure

The company analyzed commodity volatilities and correlations to produce a probabilistic analysis and derive ldquoEPS-at-riskrdquo estimates demonstrating the portion of earnings that were vulnerable to these price volatilities It then adopted a risk assessment tool capable of performing scenario analysis linked to alternative market states and specific events integrating this discipline by training both operational and financial staff in Treasury Procurement Planning and Operations This helped the company to gain insights into the aspects of its energy exposure that were most sensitive to price movements under different future states and thus were targets for mitigation efforts

Copyright copy 2011 Oliver Wyman 4

2 COMMODITY RISK MANAGEMENT FRAMEWORK

DEVELOPING A COMMODITY RISK GOVERNANCE PROGRAM

A crude oil producermarketer in Eastern Europe was recently seeking to enhance margins by creating regional physical trading capability First the Board required stakeholder alignment on a multi-dimensional risk appetite statement to guide risk and scale considerations High level loss limits were linked to the risk appetite and from these ldquodesk-levelrdquo limits were calculated By codifying the risk limits in policies the equity usage growth targets and working capital requirements were made explicit and served to support all later decisions on the business expansion plan

A structured commodity risk management framework is built around three pillars

Governance Infrastructure and Analytics Together these pillars support both short-

term tactical decisions and long-term strategic initiatives (see Exhibit 3)

GOVERNANCE

The first pillar of this framework is Governance in which an organization identifies the main

objectives of its commodity risk management program Companies first create a risk

appetite statement In this critical step an organization defines its risk tolerance and

aligns it with its broader performance objectives The risk appetite statement thus

establishes the basis of the organizationrsquos risk limits

ExhIBIT 3 COMMODITY RISK MANAGEMENT FRAMEWORK

SHORT-TERM TACTICAL DECISION MAKING

LONG-TERM STRATEGIC DECISION MAKING

GOVERNANCE

bull Risk appetite and corporate objectivesbull Risk limits bull Risk policybull Delegations of Authoritybull Decision frameworks

INFRASTRUCTURE

bull Reportingbull Accountingbull ITsystemsbull Organizational designbull Human capital

ANALYTICS

bull Price forecastingbull Commodity exposure modeling bull Stress and scenario testingbull Hedge optimization

Copyright copy 2011 Oliver Wyman 5

INFRASTRUCTURE

The second pillar Infrastructure comprises the organizational structure systems and

human capital that companies need to measure and manage risks Organizations need to

assess whether they have the systems to capture and monitor the necessary data flows The

organizational structure is important because the volatility in commodity markets requires

discipline and a tight alignment between managing cost- and revenue-related risks across

the enterprise historically these functions worked independently but today that results in

inefficiencies or even conflicting actions by different internal teams human capital is also

key In some cases a corporation may not have the experience or capabilities to manage

commodity risks effectively While a company can outsource risk management to financial

institutions or other market participants it pays a significant premium for that service and

potentially forfeits any upside potential In the process it gives its ldquopartnerrdquo proprietary

information they can use to trade for solely their own benefit

INCREASING EFFECTIVENESS ThROUGh IMPROVED INFRASTRUCTURE

The effects of steady growth were slowly compromising the risk management effectiveness of a major North American crude and distillates marketertrader Reporting timeliness was eroding error rates were climbing and managementrsquos confidence in risk control was decreasing prompting a full review of the organizational design and processes This revealed that the risk control function (middle office) was still performing much of the trade reconciliation that trade details were transmitted by email and that most reporting was done through spreadsheets

Several straightforward organizational shifts and systems enhancements eliminated a number of bottlenecks and enabled risk reporting down to the cargo level by desk and counterparty

ANALYTICS

The third pillar of a commodity risk management program is Analytics Organizations

cannot assess their financial exposure to commodity price swings without robust analytical

tools These analytics (or ldquomodelingrdquo) platforms support decision making at every level

mdash by helping managers model the future paths of commodity prices conduct stress- and

scenario-testing and evaluate and optimize risk-return profiles using a range of price-risk

management strategies however analytical capabilities vary dramatically from firm to firm

Companies should take a sequential approach to developing a robust analytics framework

that will support commodity risk management

Copyright copy 2011 Oliver Wyman 6

ThINK LIKE A TRADER

Trading is viewed as a necessary evil by some corporations given the connotations of excessive speculation and risk-taking with derivatives (in this case commodity futures contracts) A recent review of annual reports reveals that many companies disclosing commodity positions caution that these are strictly for hedging purposes and that the company ldquodoes not hold financial derivative positions for the purposes of tradingrdquo

Whether they realize it or not many companies are in fact speculating on commodity pricesmdashon both the cost and revenue sides Procurement officers are tasked with getting the best price on purchases for the company and its business units So the procurement staff commonly enters into forward fixed-price arrangements to guarantee both supply and pricing Just like traders they have thus made a bet on future prices and concluded price risk management with that view in mind

Companies need to accept that the days of a ldquoset it and forget itrdquo approach to risk management are over That approach was fine when volatility was low and prices increased gradually over time Today companies need to empower designated executives to think like traders ldquoIs this a good or bad price Should I buy more or run down inventoryrdquo Since most companies arenrsquot traders by nature they need to create a trading playbook that is integrated with their overall game plans

First a company must understand its commodity risk profile This helps the organization

assess its net exposure to commodity pricesmdashand their inevitable volatilitymdashacross

business and customer segments Detailed analysis also allows the organization to then

develop long-term strategies to mitigate commodity-related risks

A commodity risk profile provides a common understanding for senior management and

a fact-based foundation for evaluating the effectiveness of risk-mitigation actions With

this knowledge management can determine if the companyrsquos current commodity risk

exposure is within its risk tolerance and whether it has communicated these expectations

to stakeholders This analysis also helps the management team evaluate different risk

management strategies It promotes risk mitigation at the portfolio level which helps

reveal any risks lurking in individual business units or departments In short this analysis

will allow the company to optimize risk-return positioning

ThE NET ExPOSURE ChALLENGE

Determining true net exposure requires consideration of several factors gross commodity exposure existing risk mitigations foreign exchange flows demand sensitivity and the interactions among these This can be challenging for an enterprise with wide-spread operations or multiple product lines Typically each factorrsquos net impact at the corporate level is first assessed and any offsets across factors can then be accounted for

Copyright copy 2011 Oliver Wyman 7

To manage commodity price risks in ways that are consistent with broader corporate

objectives companies need a robust set of analytic tools to calculate the current exposure

Exhibit 4 offers a six-step analytical approach that companies can use to determine the

effect of commodity price risks and mitigation efforts on key financial metrics

The first step in the analytical framework requires management to build a forecast

of commodity prices using simulations or other techniques The company should

complement these forecasts with an analysis of alternative price outcomes based on ldquostress

eventsrdquo to understand fully how prices may evolve

In the second step the management team estimates the volume of future commodity

purchases across the enterprise In Step 3 this demand forecast is combined with

price projections to determine the firmrsquos gross commodity exposure In Step 4 the risk

management strategies already in place are identified Then in Step 5 these are applied

against the gross exposure to generate a ldquonet commodity exposurerdquo for the enterprise The

process ends with Step 6 the creation of a holistic company-wide risk profile ndash with the

sensitivities in price projections identified in Step 1 used to explore the potential impact on

EBITDA debt covenants and other financial metrics

Copyright copy 2011 Oliver Wyman 8

ExhIBIT 4 CREATING A hOLISTIC COMMODITY RISK PROFILE

bull Use analytic engines to simulate potential commodity price pathways (data driven)

bull Incorporate market context and paradigm shiftsscenario analysis ( judgment driven)

bull Integrates historical patterns market intelligence and fundamental analysis

bull Unifies disparate views of expected and highlow commodity price scenarios across organization

bull Incorporates interrelationships and correlations between commodities and currencies

STEP ANALYSIS BENEFITS

bull Centralizes commodity requirements across business units and geographies

bull Enables testing of alternative commodity purchase requirements using price elasticity analysis

bull Determine expected commodity purchase volume based on sales expectations

bull Gives context of expected commodity exposure compared to PampL and other risks

bull Accounts for ldquonatural hedgesrdquo in the portfolio

bull Shows shifts due to changes in business mix commodity price expectations

bull Calculate expected commodity exposure (eg price multiplied by volume)

bull Brings together and coordinates dierent functions of the organization (eg Strategy Procurement Treasury) and geographies

bull Sets understanding of risk management options for key commodity exposures

bull Define options for managing commodity price risk

bull Centralize risk management options undertaken across organization

bull Helps management assess the eectiveness of the risk management portfolio vs desired exposure

bull Provides understanding of how commodity price risk flows through the organization

bull Calculate exposure after incorporating current risk management portfolio

bull Enables objective evaluation and comparison of a range of risk management strategies

bull Promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at individual business unit or department level

bull Provides view of the earnings impact from commodity prices at dierent levels of probability

bull Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

COMMODITY PRICEPROJECTIONS

SALES PRICINGANDPURCHASE VOLUMES

GROSS EXPOSURE

RISK MANAGEMENTPORTFOLIO

NET EXPOSURE

HOLISTIC COMMODITYRISK PROFILE

Source Oliver Wyman

Copyright copy 2011 Oliver Wyman 9

IMPROVING COMPETIVENESS WITh A STRATEGIC RESPONSE TO COMMODITY VOLATILITY

A global food ingredient processor was losing sales to much smaller competitors solely because those firms were much more responsive to the highly volatile product price By analyzing the impacts of its forward sales process and market price uncertainty a new strategy was developed to migrate customers to shorter term contracts and to begin linking prices to market indices ndash which immediately improved the firmrsquos competitive positioning

STRATEGIES FOR MANAGING COMMODITY RISK

With a well-defined risk profile and an understanding of its ability to manage risk a

company can build a plan that matches its net exposure to commodities with its tolerance

for risk To manage commodity prices in the short term companies generally have three

tools at their disposal

bull Product pricingmdash identifying customer segments where the company has the ability to

raise prices or create pricing structures that mitigate risk

bull Procurement contract structuringmdashdeveloping innovative risk-sharing contracts with

suppliers

bull Financial hedgingmdashusing financial instruments for hedging to reduce overall

risk exposure

The effectiveness of these short-term strategies depends on the size of the organizationrsquos

exposure to a given commodity mdash and the commodity itself For example financial hedging

works best in commodity markets that are liquid (eg energy products such as crude oil and

natural gas or agricultural products such as wheat and corn) Meanwhile passing higher

commodity costs to customers through price increases is often ineffective in competitive

markets such as consumer products however when commodity cost increases are

significant and widespread pass-through pricing might be more viable Some companies

have taken creative approaches to raising prices For instance a number of consumer

products companies have reduced the volume of productmdashwhile keeping the same

package sizemdashto maintain margins in the face of higher commodity costs Other firms have

substituted cheaper ingredients to lower their net product costs

Over the long term volatility in commodity prices affects the behaviors of consumers

companies and their suppliers In response some companiesmdashand even countries--have

embedded commodity risk strategies into their long-term strategic plans

Copyright copy 2011 Oliver Wyman 10

CONCLUSION

Large and sustained commodity price swings are reshaping whole industries This means

that commodity risk management can no longer be considered the sole responsibility of

the procurement or finance staff With rising commodity prices affecting both the short-

term earnings and long-term strategies it is imperative that C-level executives develop a

deeper understanding of how to mitigate these risks Developing a structured commodity

risk management program built around the components outlined here is crucial Given

the new realities of higher prices and more volatility in commodities organizations must

integrate commodity risk management into their day-to-day operations They also must

build it into their long-term strategies to ensure the viability of the firm itself

ABOUT THE AUTHORS

MICHAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman

Michael J Denton PhD is a New York based Partner in Oliver Wymanrsquos Global Risk amp Trading practice with specialized experience in

energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market

risk modeling portfolio dynamics and risk-based decision frameworks Recent projects have focused on due diligence evaluations

competitive contracting and counterparty credit risk mitigation

ALEX WITTENBERG

Partner Global Risk amp Trading Oliver Wyman

Alex is the Managing Partner of Oliver Wymanrsquos Global Risk Center and has over 20 years of cross-industry experience in risk management

advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision-making and financial performance designing

risk governance for Boards and Management and developing corporate risk monitoring mitigation and transfer frameworks

Copyright copy 2011 Oliver Wyman 11

Copyright copy 2011 Oliver Wyman All rights reserved This report may not be reproduced or redistributed in whole or in part without the written permission of Oliver Wyman and Oliver Wyman accepts no liability whatsoever for the actions of third parties in this respect

The information and opinions in this report were prepared by Oliver Wyman

This report is not a substitute for tailored professional advice on how a specific financial institution should execute its strategy This report is not investment advice and should not be relied on for such advice or as a substitute for consultation with professional accountants tax legal or financial advisers Oliver Wyman has made every effort to use reliable up-to-date and comprehensive information and analysis but all information is provided without warranty of any kind express or implied Oliver Wyman disclaims any responsibility to update the information or conclusions in this report Oliver Wyman accepts no liability for any loss arising from any action taken or refrained from as a result of information contained in this report or any reports or sources of information referred to herein or for any consequential special or similar damages even if advised of the possibility of such damages

This report may not be sold without the written consent of Oliver Wyman

wwwoliverwymancom

Oliver Wyman is a leading global management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development Oliver Wymanrsquos Global Risk Center is dedicated to analyzing increasingly complex risks that are reshaping industries governments and societies Its mission is to assist decision makers in addressing risks by combining Oliver Wymanrsquos rigorous analytical approach to risk management with leading thinking from professional associations non-governmental organizations and academic institutions For further information please visit wwwoliverwymancomglobalriskcenter

The Association for Financial Professionals (AFP) headquartered outside Washington DC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFP is the daily resource for the finance profession

For more information please contact

MIChAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman +16463648423 michaeldentonoliverwymancom

ALEx WITTENBERG

Partner Global Risk amp Trading Oliver Wyman +16463648440 alexwittenbergoliverwymancom

BRIAN T KALISh

Director Finance Practice Lead +13019616564 bkalishafponlineorg

Oliver Wyman
File Attachment
Commodity volatilitypdf

intRoduCtion

given the current and future trends in commodity prices and volatility every company must

better understand its true commodity exposure Companies can often be squeezed by

rising and volatile commodity input prices that cannot be passed along to customers in

their entirety A commodity risk management program can help not all organizations can

or should adopt the sophisticated mechanisms of a pure commodity business however

most organizations particularly those in the middle of the value chain can improve their

commodity risk analytics

exhibit 1 CompAnies ARe ChAllenged by Rising Commodity volAtility

Output prices

bull Competitive pressuresbull Limited ability to pass

through higher costs

COMPANY

bull Understand current forecasts (starting position and price curve)

bull Trace impact of price volatility to earnings

bull Align commodity risk management program strategy with corporate objectives

Commodity risk management program strategy instruments etc

Input prices

bull Increasing volatilitybull Increasing absolute pricebull Breakdown in correlations

the starting point is to understand the companyrsquos holistic commodity risk profile using

analytics and modeling tools A holistic commodity risk profile helps the organization assess

its individual and net exposure to commodity pricesmdashand the inevitable volatilitymdashacross

business and customer segments on a forward-looking basis the profile provides a common

understanding for senior management and a ldquofact-basedrdquo foundation for evaluating the

effectiveness of current risk-mitigation actions and alternative risk management strategies

With this knowledge management teams can determine if current commodity risk exposure

is within the companyrsquos risk tolerance and communicate these expectations to stakeholders

it also helps to promote risk mitigation at the portfolio level by identifying the most

important drivers of overall risk and ensuring the capture of any offsetting risks that may be

present in short this analysis will allow the company to optimize risk-return positioning

this In Practice Guide provides an overview of a stepwise approach and analytic processes

necessary to develop an organizationrsquos net commodity exposure

Copyright copy 2012 oliver Wyman 3

six steps to deteRmining Commodity exposuRe And impACts

exhibit 2 provides an overview of a six-step analytical approach to determine the impact of

commodity price risks on key financial metrics

exhibit 2 six steps to CReAte A holistiC Commodity Risk pRoFile

6 HOLISTIC COMMODITY RISK PROFILE

5 NET EXPOSURE

4 RISK MANAGEMENT

PORTFOLIO

3 GROSS EXPOSURE

2 SALES PRICING AND COMMODITY

PURCHASE VOLUMES

1 COMMODITY PRICE

PROJECTIONS

STEPS

ANALYSISUse analytic engines to simulate potential commodity price pathways (data driven)

Incorporate market context and paradigm shifts scenario analysis (judgement driven)

Calculate expected commodity exposure (ie price multiplied by volume)

Define options for managing commodity price risk

Centralize risk management options undertaken across organization

Calculate exposure after incorporating current risk management portfolio

Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

Determine expected commodity purchase volume based on sales expectations

Copyright copy 2012 oliver Wyman 4

step 1 Commodity pRiCe pRojeCtions

CoRe AnAlysis user-defined baseline commodity prices and volatilities for a predefined set of commodity

inputs are incorporated into a standard simulation process to generate a distribution of

potential future price paths for each commodity

outputs A distribution of terminal price values for each commodity and time horizon

beneFits bull integrates historical patterns market intelligence and fundamental analysis

bull unifies views of commodity price scenarios across the organization

bull incorporates interrelationships and correlations between commodities and other price

risk factors (eg currencies)

the first step in the analytical framework requires

management to build assumptions for commodity

prices in the future using simulation or other techniques

the management team should complement these

forecasts with an analysis of alternative price outcomes

based on ldquostress eventsrdquo to understand fully how prices

may evolve

A simple price simulation model is presented in the

attached workbook (accessed through the pushpin

icon) the inputs for these simulations are located on

the tab labeled ldquointerfacerdquo under the heading ldquoprice

and volatilityrdquo the user defines base case prices and

volatilities for each commodity and forward period

(ie 1st quarter 2nd quarter and so on) the simulated

terminal value prices or outcomes for each commodity

and time horizon are shown on the tab labeled ldquoCmdy

price projrdquo each of these values reflects possible future

outcomes for commodity prices based on three factors

mean price volatility and time

the outputs of the simulations for each commodity are

summarized on the ldquointerfacerdquo page in the ldquoCommodity

price projectionsrdquo section in each case 5th and 95th

percentile outcomes are shown along with the baseline

price scenario over the course of the time period to

provide a richer understanding of possible outcomes

versus a single static forecast not surprisingly the

uncertainty of price forecasts grows with the increasing

time horizon

Copyright copy 2012 oliver Wyman 5

ReadMe

OverviewThe workbook has been prepared as a supplement to the In Practice Guide Six Steps to Assess Commodity Risk Exposure prepared by Oliver Wyman for AFP members and available at wwwafponlineorg The purpose of this workbook is to provide a simple tool for illustration purposes only to better understand and quantify commodity risk impacts on earmings The example focuses on a corporation which utilizes key commodities including natural gas diesel fuel sugar electricity and aluminum The components of the workbook include a primary Interface worksheet as well as support worksheets which house the calculations used to generate outputs on the Interface sheet Note that all user inputs and outputs are exclusively contained in the Interface sheet - all other sheets are for reference only Cells in white in the User Inputs block represent input cells (prices volatilities and hedge designation) Any changes in outputs are generated by using the Calculate button The Interface worksheet receives inputs and provides outputs for a probabilistic earnings forecast model and illustrates the impacts of commodity prices and volatilities on the earnings profile In particular each of the outputs reflects one of the steps in the 6 step process Outputs include(1) Commodity price projections - User-defined baseline commodity prices and volatilities for a pre-defined set of commodity inputs are incorporated into a standard simulation process to generate a distribution of potential future price paths for each commodity(2) Estimated commodity volumes - A predefined set of commodity volumes reflecting commodity demand over time(3) Gross commodity exposure - User-defined baseline prices are combined with simulated price paths and volumes to generate commodity exposure - measured as the difference between the 95th percentile worst case outcome and the baseline expected outcome (4) Net commodity exposure - Gross commodity exposure is adjusted for any financial or physical hedges for each of the commodities Hedges are assumed to lock in the commodity at the baseline expected price Price correlations between commodities are taken into account(5) Holistic commodity risk profile - Commodity exposures are combined with predefined estimates for revenues and other fixed costsSGA to illustrate both positive and negative impacts on the earnings profileKey assumptionsKey assumptions in the model include the following - A GBM process is used to simulate all of the commodity price paths - Correlations are predefined and fixed resulting in a static set of shocks used in the simulation process- User defined volumes and revenues are static- User defined prices and volatiliities are used as baseline assumptions for each quarter

Interface

Interface

REF
1

Cmdy Price Proj

Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
124418764137
1367693617635
2648580611483
786217932233
803244438443
5605736599794
124418764137
4238042982159
1367693617635
1589462370676
2648580611483
803244438443
786217932233
0
803244438443

Correlations

5
95
Mean
Sugar
01107087317
03452715728
02
00854552129
04050827214
021
00695646039
04616729635
022
0061034605
05340468686
023

Sales Pricing Volumes

Sugar
Natural gas
Electricity
Diesel
Aluminum

Gross Exposure

REF
EPS-at-risk
1

Risk Mgmt Portfolio

REF
EBITDA
1

Simulation Net Exposure

Diesel
Electricity
Natural gas
Crude oil
Price change
EPS impact
Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
1912572265773
1138117311849
1319658025171
11406289411
1162397994146
3734236225276
1912572265773
2596118913427
1138117311849
1276460888256
1319658025171
1162397994146
11406289411
0
1162397994146
5
95
Mean
Natural Gas
40688610903
79154680028
57310928297
32742030186
84855775584
57310928297
28887025303
92658501718
57310928297
26679947564
10281600624
57310928297

Net Exposure

5
95
Mean
Electricity
344141498094
1003995388957
622705433902
264877650618
1259146360173
622705433902
219723962528
1384358671012
6227
15528763279
1491896493046
622705433902

Fin Statement

5
95
Mean
Diesel
16508189205
29538139317
22444582473
14672697038
31955148416
22444582473
13074767002
36018057848
22444582473
11953891033
37258114813
22444582473

Price shock contuity

5
95
Mean
Aluminum
134769028369
254013105136
1878
119721652401
284279599425
187771063122
106347176349
316018108859
187771063122
95839609124
346925945325
187771063122

Graph support

Sugar
Natural gas
Electricity
Diesel
Aluminum
128
96
1
16
16
134
101
1
168
17
141
106
1
176
18
150
111
1
185
19
95ile
5ile
Earnings statement
-161353539101
1326569803377
611516334878
-421367106558
1798147036403
760774397443
-642177203637
2144399264928
918591594906
-1050874848209
2521161989213
1080153466317
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Correlations among commodity prices dictate the way in which commodity prices move together and are used to develop commodity price projections via simulation
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Commodity volumes may be determined by reviewing sales and demand planning forecasts Volumes may be aggregated when sourced from different business divisions or regions to arrive at a corporate-wide volume estimate
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Oliver Wyman
File Attachment
AFP-OW Commodity Risk Calculatorxls

step 2 sAles pRiCing And puRChAsing volumes

CoRe AnAlysis user-defined commodity volumes for the predefined set of commodities are coupled

with the distribution of price outcomes to support the determination of gross and net

exposures to commodities sales volumes and prices are incorporated into the pro forma

financial statement to understand earnings impacts from commodity exposures

outputs A predefined set of commodity volumes reflecting commodity demand over time

beneFits bull Centralizes commodity requirements across business units and geographies

bull enables testing of alternative commodity purchase requirements using price

elasticity analysis

in the second step the management team must

estimate the volume of future commodity purchases

across the enterprise the estimated volumes for inputs

are going to be directly linked to sales forecasts and

thus may be derived from product demand profiles

While the current model focuses on price uncertainty

it is important to recognize the presence of volume

uncertainty as well the static volumes are estimated

and then entered by the user on the tab labeled

ldquointerfacerdquo under the heading ldquoCommodity volumesrdquo

Assumptions for product sales volumes and prices are

also entered on the ldquointerfacerdquo tab for later use in the

holistic commodity risk profile assessment

step 3 gRoss exposuRe

CoRe AnAlysis user-defined baseline prices are combined with simulated price paths and volumes to

generate commodity exposure as measured by the difference between the 95th percentile

worst case outcome and the baseline expected outcome

outputs A firmrsquos gross commodity exposure given commodity price and volume

beneFits bull gives context of expected commodity exposure in comparison to pampl and other risks

bull shows changes over time due to either changes in business mix or changes in

commodity price expectations

in the third step the expected gross commodity

exposure is determined the 95th percentile worst case

commodity costs located on the tab labeled ldquogross

exposurerdquo are found by multiplying each commodity

volume by price gross exposure is determined by

summing the difference between the baseline cost and

the simulated 95th percentile worst case cost for each

individual commodity exposure Alternative percentiles

may be selected using the ldquoexposure risk levelrdquo input

value on the ldquointerfacerdquo tab

Copyright copy 2012 oliver Wyman 6

step 4 Risk mAnAgement poRtFolio

CoRe AnAlysis Risk management strategies currently in place are identified

outputs portfolio of existing risk management options (eg a set of commodity derivative hedges)

beneFits bull brings together and coordinates different functions of the organization (ie strategy

sales and marketing procurement treasury) and geographies

bull sets common understanding of risk management options for key commodity exposures

After determining gross commodity exposure the next

step in the process is to identify existing risk management

strategies the tab labeled ldquoRisk mgmt portfoliordquo has

a predefined list of risk management options available

which limit the management decision to financial

hedges that are assumed to lock-in the commodity at the

baseline expected price on the tab labeled ldquointerfacerdquo

under the heading ldquohedge selectionrdquo the user selects

ldquoyesrdquo or ldquonordquo for each commodity depending on whether

the firm plans to hedge that particular commodity it

is important to note that management of commodity

price risk is not limited only to financial hedging rather

it typically involves a combination of four broad levers

ndash each (or a combination thereof) may be effective as

illustrated in exhibit 3

besides recognizing current risk management

strategies this step also alerts management of other

potential risk mitigation options that may be available to

manage key commodities exposures

Copyright copy 2012 oliver Wyman 7

exhibit 3 Risk mAnAgement options

a Product Pricing (incl ldquoPass-throughrdquo)

b Procurement contracts

c Financial hedging

d Value chain integration long-term strategies

oVerView bull use market pricing

power to recover

change in input costs ndash

revenue management

bull balancesharetransfer

risk on purchases ndash

cost management

bull transfer risk to third

party through markets

structured deals

(Reinsurers also providing

capacity here)

bull up or downstream

integration eg

sourcing raw materials

retail trading

examPle strategies

bull employ cost pass-through

mechanisms (across

all or a portion of the

product portfolio)

bull Align price risk transfer

mechanisms with sales and

purchase contracts

bull include contract

mechanisms that mitigate

risk (eg price ceilings

floors price bands knock-

inknock-out clauses)

bull hedge price exposure

with swaps to set the price

or options or collars to

limit downside

bull Cross-hedge exposure

with contracts with

strong correlation to

underlying exposure

bull Acquire upstream company

to ensure supply and

diversify risk exposure

bull identify drivers that

contribute greatest

percentage to overall risk

Pros bull Raw materials costs fully

or partially passed through

to customers

bull stabilized margins and

earnings certainty

bull protection against price

moves and volatility

bull transition risk back

to supplier under

certain structures

bull protection against

feed stock price moves

and volatility

bull no impact on product

pricingsupplier contracts

bull holistic approach to cost

and margin management

bull diversification of

risk exposure

cons bull Customers may substitute

products or move to

lower priced competitor

brands in response to

product price

bull suppliers demand

premium (higher price)

for risk sharing

bull need to find

receptive suppliers

bull liquidity premium and

market non-availability

for many commodities

bull basis risk from

cross-hedging

bull Requires appropriate

capital and expertise

for joint ventures

or acquisitions

bull increased organizational

complexity

Copyright copy 2012 oliver Wyman 8

step 5 net exposuRe

CoRe AnAlysis predefined financial hedges and price correlations between commodities are applied to

gross commodity exposure to determine net commodity exposure for the enterprise

outputs A firmrsquos net commodity exposure after incorporating its current risk management portfolio

beneFits bull Accounts for natural hedges

bull provides management with an understanding of the effectiveness of the risk

management portfolio vs desired exposure

bull provides broad understanding of how commodity price risk flows through

the organization

While calculating net exposure management must also

take into account the correlation between commodity

prices price movements of one commodity may be related

to the price movement of other commodities thereby

increasing or decreasing exposure depending on the

relative strength of the relationship to account for this the

net exposure of the portfolio must be determined on the

tab labeled ldquosimulation net exposurerdquo the distribution

of terminal price values for each commodity and time

period (eg 1st quarter 2nd quarter etc) are multiplied

against volume for that same time period A distribution

of net exposures is generated and 5th and 95th percentile

outcomes for the portfolio series are selected

A companyrsquos net exposure to commodity price volatility is

shown on the tab labeled ldquonet exposurerdquo the predefined

volume levels are multiplied by the new correlated prices

to determine the 95th percentile worst case net exposure

if the company is employing a hedging strategy for a

commodity then it has no exposure for that particular

commodity since hedges are assumed to lock-in the

commodity at the baseline expected price

step 6 holistiC Commodity Risk pRoFile

CoRe AnAlysis Commodity exposures and price projections are combined with predefined estimates for

revenues and other fixed costssgA to illustrate both positive and negative impacts on the

earnings profile

outputs pro forma financial statement with predefined revenue and fixed costsgA reflecting the

impact of price variations and hedging on earnings

beneFits bull enables objective evaluation and comparison of a wide variety of risk

management strategies

bull promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at an

individual bu level

bull gives management a view of earnings impact from commodity prices at different levels

of probability

the process of determining net commodity exposure

ends with the creation of a holistic company-wide

risk profile ndash with the sensitivities in price projections

identified in step 1 used to explore the potential impact

on ebitdA debt covenants and other financial metrics

this impact is shown on the tab labeled lsquointerfacersquo in the

section called ldquoholistic commodity profile and impact

on earningsrdquo

Copyright copy 2012 oliver Wyman 9

ConClusion

large and sustained commodity price swings are quickly reshaping industries and the

businesses that exist within them in this environment commodity risk management must

be firmly integrated into the strategy of the company and structured under a commodity risk

management framework

executives need to recognize that their strategies may have to change to accommodate how

volatile commodity prices are redefining their competitive landscape and develop a process

to manage them across functions decisions around commodity risk management must be

based on a forward-looking view this process can include a series of tough questions about

the sustainability of business models such as

bull Will a perception of scarcity resulting from sustained volatile commodity prices impact

that commodityrsquos availability

bull Will changing commodity correlations invalidate diversification plays to hedge against

other commodities

bull Will consumers shift to cheaper private label alternatives or change their lifestyles to

avoid expenses introduced by higher commodity prices

the companies who are first to define their risk management metrics to include extreme

prices and anticipate how they can best benefit from them will have a significant

competitive advantage

Copyright copy 2012 oliver Wyman 10

Copyright copy 2012 oliver Wyman 11

For more information please contact

michael denton Partner global risk and trading oliver wyman

+16463648440 michaeldentonoliverwymancom

michael denton is a new york-based partner in oliver Wymanrsquos global Risk amp trading practice with specialized experience in energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market risk modelling portfolio dynamics and risk-based decision frameworks

alex wittenberg Partner global risk and trading oliver wyman

+16463648440 alexwittenbergoliverwymancom

Alex Wittenberg has over 20 years of cross-industry experience in risk management advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision making and financial performance designing risk governance for boards and management and developing corporate risk monitoring mitigation and transfer frameworks

brian t Kalish director Finance Practice lead

+13019616564 bkalishafponlineorg

brian t kalish is the director and Finance practice lead of the Association of Financial professionals he is responsible for the Finance practice which includes Corporate Finance Risk management Capital markets investor Relations Financial planning amp Analysis Accounting and Financial Reporting

oliveR WymAn And the AssoCiAtion FoR FinAnCiAl pRoFessionAls the FinAnCiAl insights seRies

this is part of series of papers by international management consulting firm oliver Wyman and the Association for Financial professionals that address critical issues for finance professionals oliver Wyman and AFp welcome your thoughts on the ideas presented in this paper if you would like to provide feedback or be alerted when we schedule events or other activities for corporate finance professionals please contact one of the individuals listed below

oliver Wyman is an international management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development

the Association for Financial professionals (AFp) headquartered outside Washington dC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFp is the daily resource for the finance profession

wwwoliverwymancom

Copyright copy oliver Wyman All rights reserved

AuthoRs

michael denton Partner global risk and trading oliver wyman

alex wittenberg Partner global risk and trading oliver wyman

brian t Kalish director Finance Practice lead

Gross Exposure graph support (stacked graph and waterfall)
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 5606 4238 1589 803 00
Grey 1244 1368 2649 786 803
Sugar 1244 186 261 341 456 1244
Natural gas 1368 210 278 375 505 1368
Electricity 2649 381 636 762 869 2649
Diesel 786 113 160 239 274 786
Aluminum 803 106 164 231 302 803
Commodities volume graph support
Q1 Q2 Q3 Q4
Sugar 12800 13400 14100 15000
Natural gas 960 1010 1060 1110
Electricity 100 100 100 100
Diesel 1600 1680 1760 1850
Aluminum 160 170 180 190
Net exposure graph support
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 3734 2596 1276 1162 00
Grey 1913 1138 1320 114 1162
Sugar 1913 207 404 530 771 1913
Natural gas 1138 168 250 348 372 1138
Electricity 1320 221 266 331 502 1320
Diesel 114 26 39 12 38 114
Aluminum 1162 151 223 340 448 1162
Financial statement graph support
Cost (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 463 686 840 1116 3105
Volume (lbs) 1280 1340 1410 1500 463 686 840 1116 3105 Unhedged
Price 036 051 060 074
Natural gas 718 829 956 1008 718 829 956 1008 3511
Volume 96 101 106 111 718 829 956 1008 3511 Unhedged
Price 75 82 90 91
Electricity 844 888 953 1125 844 888 953 1125 3810
Volume 10 10 10 10 844 888 953 1125 3810 Unhedged
Price 844 888 953 1125
Diesel 385 416 407 453 385 416 407 453 1660
Volume 160 168 176 185 385 416 407 453 1660 Unhedged
Price 24 25 23 24
Aluminum 451 543 678 805 451 543 678 805 2477
Volume 16 17 18 19 451 543 678 805 2476841735006 Unhedged
Price 282 319 377 424
Total 2861 3361 3834 4507 2861 3361 3834 4507 14564
2861 3361 3834 4507 14563772697504 Unhedged
Cost (5)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 179 142 141 119 179 142 141 119 581
Volume (lbs) 1280 1340 1410 1500 179 142 141 119 581 Unhedged
Price 014 011 010 008
Natural gas 347 283 251 241 347 283 251 241 1123
Volume 96 101 106 111 347 283 251 241 1123 Unhedged
Price 36 28 24 22
Electricity 332 223 176 138 332 223 176 138 868
Volume 10 10 10 10 332 223 176 138 868 Unhedged
Price 332 223 176 138
Diesel 275 264 244 231 275 264 244 231 1015
Volume 160 168 176 185 275 264 244 231 1015 Unhedged
Price 17 16 14 12
Aluminum 241 230 236 205 241 230 236 205 911
Volume 16 17 18 19 241 230 236 205 911127595797 Unhedged
Price 150 135 131 108
Total 1373 1142 1048 935 1373 1142 1048 935 4498
95ile 5ile
Sales 480 504 529 556 480 504 529 556
Fixed Costs 120 120 120 120 120 120 120 120
Variable Costs 286 336 383 451 137 114 105 93
General amp Admin Exp 90 90 90 90 90 90 90 90
EBITDA (16) (42) (64) (105) 133 180 214 252
Title To quickly check to make sure prices are still the same filter the prices on the tab proj 3 to go in order 1 to 100 and this should match
[Optional comments]
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
Earnings statement
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015
000
Cost of Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($lbs) 2560 2814 3102 3450 11926
Volume (lbs) 12800 13400 14100 15000
Price 020 021 022 023
000
Natural gas ($Kcf) - Henry Hub 5502 5788 6075 6362 23727
Volume 960 1010 1060 1110
Price 573 573 573 573
000
Electricity ($MWh) - NY ISO 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($gal) - NY Harbour 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
000
General amp Admin Exp 9000 9000 9000 9000 36000
000
EBITDA 6115 7608 9186 10802 33710
Impact of hedging
all figures in millions (except prices)
Futures Used
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
Net exposure (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 207 404 530 771 1913
Volume (lbs) 1280 1340 1410 1500
Price 036 051 060 074
00 00 00 00 00
Natural gas 718 829 956 1008 168 250 348 372 1138
Volume 96 101 106 111
Price 75 82 90 91
00 00 00 00 00
Electricity 844 888 953 1125 221 266 331 502 1320
Volume 10 10 10 10
Price 844 888 953 1125
00 00 00 00 00
Diesel 385 416 407 453 26 39 12 38 114
Volume 160 168 176 185
Price 24 25 23 24
00 00 00 00 00
Aluminum 451 543 678 805 151 223 340 448 1162
Volume 16 17 18 19
Price 282 319 377 424
Total 2861 3361 3834 4507 773 1182 1561 2131 5647
Correlated Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum Total Net Exposure
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Full-Year
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Volume 12800 13400 14100 15000 960 1010 1060 1110 100 100 100 100 1600 1680 1760 1850 160 170 180 190
Simulated
5 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 48715
95 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145722
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
Mean
Net Exposure Net Portfolio Exposure
32380 47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739 32380
33948 42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789 33948
39948 55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685 39948
43542 60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103 43542
44977 68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 44977
48911 50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956 48911
49824 97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168 49824
52614 9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173 52614
54247 89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834 54247
54467 29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518 54467
56081 37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959 56081
56950 66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358 56950
58740 44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783 58740
59233 63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890 59233
60239 11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751 60239
60316 3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213 60316
61664 56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573 61664
65269 4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321 65269
65273 92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204 65273
65764 69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759 65764
65876 93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008 65876
66078 87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886 66078
66116 74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632 66116
66562 28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686 66562
66781 5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205 66781
68244 27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291 68244
69290 94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559 69290
69701 32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209 69701
70796 10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158 70796
71222 62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991 71222
71261 45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691 71261
72702 91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228 72702
73734 20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550 73734
73801 52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806 73801
74108 88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414 74108
74317 1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024 74317
74660 22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160 74660
74808 31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368 74808
74998 82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171 74998
75350 8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146 75350
75850 85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158 75850
76270 2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409 76270
78037 30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375 78037
78226 23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103 78226
81022 96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662 81022
82068 99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748 82068
82643 35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858 82643
87060 14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447 87060
87614 83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068 87614
89614 46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928 89614
90297 100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267 90297
91492 49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762 91492
92285 79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150 92285
92517 80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079 92517
92520 58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466 92520
93297 38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511 93297
93492 84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782 93492
94010 72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702 94010
95355 25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982 95355
95981 51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310 95981
96590 70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430 96590
96659 59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987 96659
98984 39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335 98984
99473 64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881 99473
100438 18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643 100438
104925 26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805 104925
106683 71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274 106683
107046 48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657 107046
108929 75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922 108929
109789 17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544 109789
109940 98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393 109940
111963 61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121 111963
113140 41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558 113140
114475 15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927 114475
115420 76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479 115420
115470 12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839 115470
115543 43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938 115543
115599 16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233 115599
115628 95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056 115628
115695 13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336 115695
116747 78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058 116747
118794 33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808 118794
120798 67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960 120798
122134 7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729 122134
123516 73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263 123516
125606 90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639 125606
126928 54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417 126928
128846 77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264 128846
128878 6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513 128878
130807 34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239 130807
140398 19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517 140398
142414 81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603 142414
143917 53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292 143917
145005 65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126 145005
145638 36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145638
147330 40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883 147330
149955 86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437 149955
167385 24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467 167385
178469 57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836 178469
195830 21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430 195830
Risk management portfolio
Futures Description
Sugar Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Natural gas Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Electricity Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Diesel Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Aluminum Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Gross exposure calculations
Gross Exposure
all figures in millions (except prices) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 442 543 651 801 186 261 341 456 1244
Volume (lbs) 1280 1340 1410 1500
Price 035 041 046 053
00 00 00 00 00
Natural gas 760 857 982 1141 210 278 375 505 1368
Volume (MMBTU) 96 101 106 111
Price 792 849 927 1028
00 00 00 00 00
Electricity 1004 1259 1384 1492 381 636 762 869 2649
Volume (MWh) 10 10 10 10
Price 1004 1259 1384 1492
00 00 00 00 00
Diesel 473 537 634 689 113 160 239 274 786
Volume (gal) 160 168 176 185
Price 295 320 360 373
00 00 00 00 00
Aluminum 406 483 569 659 106 164 231 302 803
Volume (tons) 16 17 18 19
Price 2540 2843 3160 3469
Total 3085 3679 4220 4783 996 1500 1947 2407 6850
Sales Pricing and Volumes
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015 016
000
Cost of Commodity Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($) 2560 2814 3102 3450 11926
Volume (lb) 12800 13400 14100 15000
($lb) 020 021 022 023
000
Natural gas ($) 5502 5788 6075 6362 23727
Volume (MMBTU) 960 1010 1060 1110
Price ($MMBTU) 573 573 573 573
000
Electricity ($) 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($l) 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum ($) 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
Correlations
Correlated random shocks generated from correlation matrix
Correlations Sugar Natural gas Electricity Diesel Aluminum
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Sugar Q1 100 099 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q2 099 100 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q3 099 099 100 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q4 099 099 099 100 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Nat Gas Q1 065 065 065 065 100 099 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q2 065 065 065 065 099 100 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q3 065 065 065 065 099 099 100 099 094 094 094 094 086 086 086 086 069 069 069 069
Q4 065 065 065 065 099 099 099 100 094 094 094 094 086 086 086 086 069 069 069 069
Electricity Q1 055 055 055 055 094 094 094 094 100 099 099 099 082 082 082 082 071 071 071 071
Q2 055 055 055 055 094 094 094 094 099 100 099 099 082 082 082 082 071 071 071 071
Q3 055 055 055 055 094 094 094 094 099 099 100 099 082 082 082 082 071 071 071 071
Q4 055 055 055 055 094 094 094 094 099 099 099 100 082 082 082 082 071 071 071 071
Diesel Q1 047 047 047 047 086 086 086 086 082 082 082 082 100 099 099 099 066 066 066 066
Q2 047 047 047 047 086 086 086 086 082 082 082 082 099 100 099 099 066 066 066 066
Q3 047 047 047 047 086 086 086 086 082 082 082 082 099 099 100 099 066 066 066 066
Q4 047 047 047 047 086 086 086 086 082 082 082 082 099 099 099 100 066 066 066 066
Aluminum Q1 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 100 099 099 099
Q2 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 100 099 099
Q3 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 100 099
Q4 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 099 100
Column C C C C G G G G - 0 K K K - 0 O O O S S S S
Indirect C_Prices - 0 - 0
Correlated Sugar Natural gas Electricity Diesel Aluminum
N(01) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1 (108) (092) (082) (107) (047) (078) (057) (062) (015) (003) (008) (027) 041 052 041 022 052 051 036 039
2 031 (007) (006) (003) (015) (014) (012) (009) (033) (017) (021) (009) 005 005 026 (009) (087) (103) (079) (074)
3 (176) (167) (159) (155) (092) (079) (090) (098) (058) (061) (057) (069) (009) 018 007 002 (124) (101) (128) (121)
4 (134) (119) (112) (122) (046) (044) (043) (052) (066) (052) (037) (044) 016 009 (016) (003) (106) (116) (111) (094)
5 (075) (080) (049) (077) (063) (028) (057) (072) (045) (056) (051) (042) 011 010 021 032 (135) (127) (130) (123)
6 082 082 072 095 137 110 115 132 119 098 119 143 095 104 085 108 092 107 098 107
7 035 050 038 044 098 108 105 100 140 129 127 131 123 119 119 139 (024) (022) (013) (010)
8 (056) (044) (076) (066) (017) (033) (033) (045) (009) (007) 003 016 (091) (074) (102) (071) 078 068 080 058
9 (025) (015) (023) (027) (104) (127) (109) (105) (135) (150) (144) (166) (149) (148) (165) (151) (130) (126) (134) (131)
10 126 116 115 112 (120) (097) (107) (114) (054) (046) (052) (053) (107) (099) (125) (099) (117) (120) (125) (135)
11 (166) (182) (173) (174) (090) (083) (095) (089) (061) (070) (065) (060) (066) (048) (061) (084) 006 (013) (002) (006)
12 014 014 034 034 074 062 073 046 131 148 164 155 003 (003) 002 (035) 006 (003) 000 010
13 034 028 011 035 142 130 113 120 118 112 086 091 060 030 044 052 085 081 088 084
14 (048) (059) (063) (068) 033 026 020 050 021 017 019 023 001 (012) 009 005 089 097 075 099
15 079 091 090 091 083 082 073 081 096 086 093 081 107 112 115 096 047 038 030 024
16 058 071 034 059 116 117 122 103 064 084 084 092 081 080 068 108 093 092 082 070
17 (046) (041) (043) (048) 099 112 121 101 138 111 102 126 031 049 027 052 (019) (030) (033) (045)
18 030 052 025 024 060 078 068 059 073 093 069 070 031 033 026 030 (020) (037) (024) (026)
19 (034) (031) (025) (026) 146 114 124 151 166 173 175 161 121 116 108 097 222 227 201 212
20 (119) (138) (136) (114) (009) (009) 003 (012) (048) (021) (028) (040) 073 048 037 039 (028) (009) (003) (044)
21 092 081 085 085 236 228 257 241 229 231 230 239 259 264 269 263 098 082 075 097
22 047 052 031 042 (014) (030) (006) (016) (028) (027) (041) (031) (035) (040) (014) (031) (122) (127) (135) (134)
23 (043) (047) (046) (069) (021) (019) (014) (044) (005) 020 005 (006) (032) (025) (021) (012) 075 060 052 051
24 082 083 064 089 195 192 202 195 198 209 195 198 138 109 128 113 201 194 206 208
25 039 030 035 039 042 037 048 054 067 069 066 059 (050) (065) (051) (061) 032 038 040 033
26 130 157 113 138 057 052 036 048 076 085 063 076 (053) (058) (061) (052) 009 004 (012) 004
27 (063) (063) (067) (046) (041) (045) (053) (035) 012 015 004 (003) (140) (136) (152) (120) (116) (127) (101) (101)
28 (051) (067) (068) (058) (075) (063) (051) (068) (056) (050) (053) (068) (031) (025) (019) (053) (022) (005) (014) (018)
29 (165) (163) (176) (155) (115) (130) (143) (128) (065) (067) (081) (085) (128) (105) (126) (127) (058) (047) (031) (050)
30 (029) (031) (029) (032) (007) (016) (019) (003) 010 018 005 012 001 017 007 (007) (077) (089) (071) (081)
31 (040) (055) (058) (043) (004) (009) (010) (041) (007) (023) (027) (028) (065) (082) (086) (075) 078 068 065 089
32 123 098 102 105 (047) (059) (069) (079) (101) (106) (094) (097) (069) (074) (072) (089) (125) (131) (115) (122)
33 057 052 066 070 082 074 077 075 085 082 081 079 079 090 068 091 245 239 238 237
34 174 156 162 156 142 131 136 135 081 071 075 086 143 133 150 126 046 052 053 071
35 (121) (127) (111) (122) (004) (005) 018 (009) 051 059 046 032 (021) 003 (018) (008) 034 046 012 013
36 187 205 195 203 123 123 130 120 105 097 101 120 046 051 025 042 262 246 265 270
37 (019) (031) (029) (029) (106) (097) (106) (108) (103) (098) (084) (097) (090) (092) (107) (106) (199) (187) (190) (194)
38 080 080 081 069 018 036 (001) 014 (002) (022) (018) (017) 074 071 073 069 109 102 092 107
39 040 053 044 058 085 065 070 063 020 040 029 023 029 053 042 047 076 059 077 084
40 085 100 108 089 134 123 111 133 158 169 164 168 151 138 173 169 149 174 152 150
41 082 097 061 076 101 085 105 108 092 079 093 076 111 111 112 113 (045) (064) (044) (042)
42 (123) (134) (135) (147) (263) (271) (274) (254) (270) (276) (279) (268) (210) (223) (208) (227) (245) (244) (236) (255)
43 (016) (018) (018) (034) 100 092 094 115 122 134 110 112 115 109 102 111 027 024 035 026
44 (048) (052) (042) (035) (120) (122) (121) (119) (085) (098) (098) (095) (112) (115) (120) (132) (006) 005 001 (000)
45 069 067 059 054 (073) (073) (070) (067) (057) (073) (070) (080) (033) (026) (039) (057) (045) (040) (018) (017)
46 005 011 013 005 014 048 036 037 031 036 041 047 (043) (018) (052) (039) 030 037 050 024
47 (235) (256) (232) (222) (247) (231) (242) (221) (232) (219) (219) (235) (272) (277) (282) (278) (312) (301) (287) (275)
48 143 131 119 126 084 106 097 108 (004) 017 025 021 087 072 067 071 (004) 001 (000) (023)
49 (063) (045) (054) (035) 023 021 028 027 075 105 080 081 (048) (052) (049) (045) (006) 001 002 (004)
50 (055) (060) (066) (079) (129) (132) (138) (130) (154) (145) (127) (155) (157) (121) (133) (121) (205) (205) (217) (195)
51 039 028 044 031 052 056 043 052 030 025 046 023 035 034 022 042 079 079 067 081
52 (087) (075) (088) (067) (009) 002 (016) (002) 007 (011) (013) 000 (087) (077) (079) (081) (001) 016 (007) 004
53 (032) (024) (025) (035) 149 156 161 156 156 170 148 179 148 147 170 141 170 188 173 191
54 186 179 168 155 124 117 143 121 053 066 061 058 106 101 095 112 093 084 097 095
55 (135) (144) (137) (150) (190) (192) (201) (188) (170) (167) (161) (174) (185) (199) (201) (171) (296) (281) (273) (299)
56 (052) (051) (058) (040) (075) (083) (084) (087) (111) (124) (115) (109) (019) (009) 002 (019) (046) (047) (058) (039)
57 173 174 186 206 185 163 174 165 179 199 202 204 095 113 095 113 240 264 251 239
58 076 087 072 084 039 077 056 072 (005) 026 004 024 008 (004) (018) (002) (056) (058) (067) (061)
59 (072) (037) (056) (072) 059 073 063 057 048 051 060 051 074 102 100 080 033 037 022 034
60 (109) (093) (104) (106) (170) (175) (156) (156) (192) (185) (165) (178) (238) (234) (237) (243) (122) (127) (109) (150)
61 044 048 032 047 104 106 096 106 063 077 086 081 102 092 089 080 079 079 059 054
62 (105) (101) (086) (094) 009 (006) (009) (007) (027) (025) (023) (025) 026 015 030 041 (161) (157) (166) (184)
63 (095) (103) (105) (122) (077) (072) (095) (081) (123) (108) (120) (115) (077) (075) (081) (075) 020 018 028 018
64 (079) (070) (083) (086) 074 070 060 069 062 065 077 059 121 103 119 133 016 011 (005) 017
65 096 093 106 097 146 131 129 142 147 140 150 146 169 166 156 154 176 197 186 175
66 (049) (046) (037) (042) (084) (088) (081) (084) (110) (125) (112) (120) (149) (140) (151) (124) (042) (051) (064) (085)
67 061 045 052 064 096 126 115 098 107 100 105 103 157 142 150 155 009 034 031 029
68 (085) (114) (100) (117) (180) (194) (192) (178) (165) (186) (182) (184) (135) (123) (134) (140) (131) (134) (116) (157)
69 (084) (062) (057) (060) (071) (050) (052) (053) (104) (110) (109) (100) 028 002 031 008 (003) (012) (008) (004)
70 (002) 011 007 002 058 065 058 057 042 027 035 034 069 050 045 037 066 076 106 097
71 023 024 035 027 075 066 071 081 095 084 089 077 039 040 032 039 076 096 076 076
72 (005) (026) (008) (013) 061 048 062 036 027 026 034 017 121 143 116 129 (056) (025) (024) (015)
73 048 050 056 053 118 111 110 109 097 090 099 083 203 196 178 200 072 102 084 074
74 (119) (122) (110) (137) (040) (041) (045) (036) (033) (031) (018) (030) (071) (076) (086) (093) (044) (057) (050) (028)
75 114 095 103 110 098 087 111 092 026 041 026 023 109 106 108 120 027 029 017 023
76 057 048 022 022 085 080 076 090 086 072 090 077 167 169 163 169 098 084 086 103
77 190 171 152 155 112 132 124 105 111 085 105 108 040 053 065 059 046 074 069 075
78 077 059 048 077 080 079 090 080 111 120 103 119 089 077 070 073 059 064 042 045
79 118 100 116 107 005 (002) 006 011 010 031 016 013 (033) (034) (056) (059) 046 050 062 058
80 033 017 045 032 047 053 045 050 (010) 004 (012) (001) 066 093 082 077 045 034 044 048
81 182 197 190 205 085 101 107 099 114 122 103 125 081 073 071 071 197 192 209 220
82 076 081 062 078 (080) (086) (091) (084) (093) (085) (087) (086) (006) (014) (000) 009 082 069 074 061
83 156 153 147 166 006 (004) 020 003 (019) (018) (026) (025) (052) (052) (037) (048) (178) (171) (159) (160)
84 017 (010) (000) 003 048 058 066 040 042 051 037 048 020 052 037 030 006 006 (001) (000)
85 006 007 006 (000) 009 (020) (017) (013) 010 005 007 023 (045) (064) (063) (069) (131) (148) (138) (135)
86 078 080 076 077 162 150 131 145 144 154 144 151 281 273 262 269 091 085 107 097
87 (034) (019) (010) (011) (080) (060) (085) (074) (086) (087) (093) (097) (024) (034) (033) (036) (001) 001 010 017
88 (070) (046) (064) (058) (028) (040) (049) (046) (009) 005 (021) (021) 038 056 036 033 (045) (052) (050) (073)
89 (031) (037) (025) (013) (099) (097) (094) (098) (097) (107) (096) (084) (158) (167) (144) (163) (221) (230) (232) (238)
90 123 130 136 128 057 060 088 099 103 109 105 089 112 116 115 112 133 118 133 122
91 (146) (126) (133) (132) (037) (026) (029) (035) (010) (020) (038) (029) 000 (024) (020) (022) 059 068 077 069
92 (001) 007 (009) 018 (073) (053) (058) (058) (065) (078) (068) (070) (048) (052) (051) (052) (112) (143) (127) (123)
93 (226) (202) (225) (216) (056) (079) (073) (055) (020) (046) (045) (052) (011) (017) (014) (016) 027 021 039 037
94 (131) (113) (112) (115) (025) (019) (023) (024) (021) (013) (037) (047) (034) (005) (048) (023) (037) (042) (041) (042)
95 (087) (084) (091) (090) 066 068 041 072 116 108 119 121 152 144 136 136 152 180 159 168
96 (042) (035) (040) (038) (018) (001) (010) (018) 011 004 025 008 031 040 035 039 (011) (013) (012) (022)
97 (151) (157) (167) (162) (122) (147) (143) (134) (141) (132) (114) (134) (093) (074) (093) (093) (108) (119) (109) (132)
98 096 101 091 109 097 102 101 107 070 074 071 040 097 098 089 099 (062) (089) (089) (077)
99 033 028 029 021 (008) (017) (022) (009) (001) (010) 018 (002) 027 008 007 033 (038) (031) (030) (006)
100 (055) (051) (066) (067) 011 011 029 005 018 022 034 038 091 085 091 088 073 071 087 075
Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Simulated
5 011 009 007 006 407 327 289 267 3441 2649 2197 1553 165 147 131 120 1348 1197 1063 958
95 035 041 046 053 792 849 927 1028 10040 12591 13844 14919 295 320 360 373 2540 2843 3160 3469
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024
2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409
3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213
4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321
5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205
6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513
7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729
8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146
9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173
10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158
11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751
12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839
13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336
14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447
15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927
16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233
17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544
18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643
19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517
20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550
21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430
22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160
23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103
24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467
25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982
26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805
27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291
28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686
29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518
30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375
31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368
32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209
33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808
34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239
35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858
36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238
37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959
38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511
39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335
40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883
41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558
42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789
43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938
44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783
45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691
46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928
47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739
48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657
49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762
50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956
51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310
52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806
53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292
54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417
55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685
56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573
57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836
58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466
59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987
60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103
61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121
62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991
63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890
64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881
65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126
66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358
67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960
68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081
69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759
70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430
71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274
72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702
73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263
74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632
75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922
76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479
77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264
78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058
79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150
80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079
81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603
82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171
83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068
84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782
85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158
86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437
87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886
88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414
89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834
90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639
91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228
92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204
93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008
94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559
95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056
96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662
97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168
98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393
99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748
100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1
1
185947613236 209700016612 381289955056 113496909508 105940968217
261410846662 278202957602 636440926272 159777507841 164064511715
340758878594 374684278258 761658671012 238893166595 230844682326
456070302878 505106365162 869191059145 27405034829 302394276185
1 1 1
2 2 2
3 3 3
4 4 4
2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure
Sugar Sugar Sugar Sugar Sugar Sugar Sugar
Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas
Electricity Electricity Electricity Electricity Electricity Electricity Electricity
Diesel Diesel Diesel Diesel Diesel Diesel Diesel
Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum
1
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
USER INPUTS RISK PROFILE OUTPUTS
Date 1112 5 Holistic commodity profile and impact on earnings (baseline 95 confidence band 5 confidence band)
Price and Volatility Base 5th -ile 95th -ile
Q1 Q2 Q3 Q4 2012 Un hedged Hedged Impact Un hedged Hedged Impact
Price Volatility Price Volatlity Price Volatility Price Volatility of hedge of hedge
Sugar ($lbs) $ 020 70 $ 021 70 $ 022 70 $ 023 70 Sales 2069 2069 2069 - 0 2069 2069 - 0
Natural gas ($MMBTU) $ 573 48 $ 573 48 $ 573 48 $ 573 48 Cost of Goods Sold
Electricity ($MWh) $ 6227 69 $ 6227 69 $ 6227 69 $ 6227 69 Fixed Costs 480 480 480 - 0 480 480 - 0
Diesel ($gal) $ 224 37 $ 224 37 $ 224 37 $ 224 37 Sugar 119 58 58 - 0 311 311 - 0
Aluminum ($KTon) $ 1878 32 $ 1878 32 $ 1878 32 $ 1878 32 Natural gas 237 112 112 - 0 351 351 - 0
Electricity 249 87 87 - 0 381 381 - 0
Exposure risk level 95 Diesel 155 101 101 - 0 166 166 - 0
Aluminum 131 91 91 - 0 248 248 - 0
Commodity volumes (in millions) Variable Costs 892 450 450 - 0 1456 1456 - 0
Q1 Q2 Q3 Q4 Total COGS 1372 930 930 - 0 1936 1936 - 0
Sugar (lbs) 1280 1340 1410 1500 Gen amp Admin Exp 360 360 360 - 0 360 360 - 0
Natural gas (MMBTU) 96 101 106 111 EBITDA 337 779 779 - 0 (228) (228) - 0
Electricity (MWh) 10 10 10 10
Diesel (gal) 160 168 176 185
Aluminum (KTon) 16 17 18 19 4 Net commodity exposure 2012 by commodity (in millions $)
Other financial statement assumptions
Q1 Q2 Q3 Q4
Sales volume (in millions) 3200 3360 3528 3704
Sales price ($) 015 015 015 015
Fixed costs ($ millions) 120 120 120 120
GampA Exp ($ millions) 90 90 90 90
Hedge Selection
Futures
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
3 Gross commodity exposure 2012 (in millions $)
a) By quarter b) By commodity
All exposures reported as the difference between the 95th percentile highest exposure and the expected baseline gross commodity exposure
2 Estimated commodity volumes for 2012 by quarters (variable units in millions $)
1 Commodity price projections (baseline 95 confidence band 5 confidence band)
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
Read Me - File Overview
Page 3: Commodity Risk Management - Oliver Wyman

Global Risk Center

VOLATILITY NOT VULNERABILITYTO SURVIVE SWINGS IN COMMODITY PRICES COMPANIES MUST FIRST BUILD A RISK-MANAGEMENT FRAMEWORK

Historically most businesses could simply withstand changes in commodity prices given that the swings were usually temporary cyclicalmdashand manageable However structural changes in the global economy are creating wilder swings in commodity prices that are not only affecting short-term profits but also long-term planning and investment In this environment every company must develop a formal risk management approach to counter the growing volatility in commodity prices For corporate leaders this means building the infrastructure governance programs and analytical capabilities that can help them better manage their exposure to commodities

1 COMMODITY PRICES AND VOLATILITY

After a brief respite in 2009 the volatility that has come to define many commodity markets

roared back in the latter half of 2010mdashmuch to the dismay of businesses for whom oil

industrial metals and other raw materials comprise a significant share of their costs Crude

oil cracked the psychological barrier of $100 per barrel major food price indexes reached

record highs in the first quarter of 2011 and base and industrial metals such as copper and

aluminum also reached new highs mdashcreating significant pain for buyers (see Exhibit 1) Even

where prices pulled back the cost of many commodities remained higher than beforemdashthe

result of structural shifts in supply and demand on both a local and global scale (For a closer

look at the causes of volatility see Exhibit 2)

These commodity shocks are not only cutting into corporate profits but are testing the

abilities of even the best businesses to plan for and invest in the future Already in 2011 the

CEOs of consumer-facing companies as diverse as PepsiCo Kraft Kimberly-Clark and Levirsquos

have said they expect commodity price inflation to pose a significant challenge to continued

earnings growth over the next several years Those in the middle of the value chain have a

tougher challenge as they cannot easily raise prices to recover lost margins Few expect this

storm to pass quickly The 2011 World Economic Forum Global Risks Survey found corporate

executives in agreement that a key risk they face in the coming decade is extreme volatility in

energy and other commodity prices1

1 World Economic Forum ldquoGlobal Risks 2011rdquo 6th edition January 2011 Oliver Wyman was a contributor to that report

EXHIBIT 1 COMMODITY PRICES ARE SOARING INDEX

8

12

10

6

4

2

0

14

MULTIPLE OF INITIAL PRICEREAL PRICE HISTORY

1987 1991 1995 2003 20111999 2007

Crude Oil

Copper

Wheat

source Datastream 2011

Copyright copy 2011 Oliver Wyman 3

ExhIBIT 2 WhY ThE VOLATILITY

CAUSE DESCRIPTION

Erratic weather Catastrophic weather events have affected production (2009 drought in Australia affected global wheat prices)

Emerging markets Growth in emerging markets has increased demand for food energy and raw materials Global food output will have to rise 70 by 2050 to meet demand while global energy demand is predicted to increase by 36 between 2008-2035

Speculation Emergence of commodities as an investment class (development of commodities-based ETFs)

Infrastructure spending Deteriorating infrastructure in developed countries and a lack of infrastructure in emerging economies hampers the physical flow of commodities

Supply-country strategies Development of market-distorting trade policies (Russian wheat export ban followed crop shortfall in 2010)

Purchasing-country strategies

Countries developing more sophisticated buying capabilities (Korea opened a grain-trading office in Chicago in 2011)

Food and Agriculture Organization of the United Nations World Summit on Food Security November 2009

International Energy Agency World Energy Outlook 2010

This volatility could persist for years particularly given that governments appear willing to disrupt or intervene in markets including halting exports These collective forces have created pressure for corporations to develop strategies to mitigate this growing risk While companies in agri-processing oil refining and wholesale electricity generation have developed formal approaches to trading and risk management programs the majority of companies need to do moremdashmuch more

Given the likelihood of further volatility in commodity prices every company must adopt an analytic risk framework based on a clear understanding of its exposure This paper offers guidance on developing a plan for managing commodity risks that draws on the best practices of companies from around the world The approach is built around the three pillars of a ldquobest practicesrdquo program governance infrastructure and analytics The paper provides an in-depth review of the role of analytics and outlines a new systematic approach to managing commodity risk illustrated through case studies

ONE COMPANYrsquoS MOVES TO TAME ITS VULNERABILITY TO VOLATILITY

A leading industrial company failed to deliver its projected earnings due to a decline in the profitability of non-core energy activities To measure the companyrsquos total exposure to energy volatilitymdashand quantify the potential effect on future profits it needed to develop an integrated energy risk profile In other words the company needed the ability to aggregate the energy exposure of each business unit and evaluate the effectiveness of existing mitigation efforts before it could truly understand net exposure

The company analyzed commodity volatilities and correlations to produce a probabilistic analysis and derive ldquoEPS-at-riskrdquo estimates demonstrating the portion of earnings that were vulnerable to these price volatilities It then adopted a risk assessment tool capable of performing scenario analysis linked to alternative market states and specific events integrating this discipline by training both operational and financial staff in Treasury Procurement Planning and Operations This helped the company to gain insights into the aspects of its energy exposure that were most sensitive to price movements under different future states and thus were targets for mitigation efforts

Copyright copy 2011 Oliver Wyman 4

2 COMMODITY RISK MANAGEMENT FRAMEWORK

DEVELOPING A COMMODITY RISK GOVERNANCE PROGRAM

A crude oil producermarketer in Eastern Europe was recently seeking to enhance margins by creating regional physical trading capability First the Board required stakeholder alignment on a multi-dimensional risk appetite statement to guide risk and scale considerations High level loss limits were linked to the risk appetite and from these ldquodesk-levelrdquo limits were calculated By codifying the risk limits in policies the equity usage growth targets and working capital requirements were made explicit and served to support all later decisions on the business expansion plan

A structured commodity risk management framework is built around three pillars

Governance Infrastructure and Analytics Together these pillars support both short-

term tactical decisions and long-term strategic initiatives (see Exhibit 3)

GOVERNANCE

The first pillar of this framework is Governance in which an organization identifies the main

objectives of its commodity risk management program Companies first create a risk

appetite statement In this critical step an organization defines its risk tolerance and

aligns it with its broader performance objectives The risk appetite statement thus

establishes the basis of the organizationrsquos risk limits

ExhIBIT 3 COMMODITY RISK MANAGEMENT FRAMEWORK

SHORT-TERM TACTICAL DECISION MAKING

LONG-TERM STRATEGIC DECISION MAKING

GOVERNANCE

bull Risk appetite and corporate objectivesbull Risk limits bull Risk policybull Delegations of Authoritybull Decision frameworks

INFRASTRUCTURE

bull Reportingbull Accountingbull ITsystemsbull Organizational designbull Human capital

ANALYTICS

bull Price forecastingbull Commodity exposure modeling bull Stress and scenario testingbull Hedge optimization

Copyright copy 2011 Oliver Wyman 5

INFRASTRUCTURE

The second pillar Infrastructure comprises the organizational structure systems and

human capital that companies need to measure and manage risks Organizations need to

assess whether they have the systems to capture and monitor the necessary data flows The

organizational structure is important because the volatility in commodity markets requires

discipline and a tight alignment between managing cost- and revenue-related risks across

the enterprise historically these functions worked independently but today that results in

inefficiencies or even conflicting actions by different internal teams human capital is also

key In some cases a corporation may not have the experience or capabilities to manage

commodity risks effectively While a company can outsource risk management to financial

institutions or other market participants it pays a significant premium for that service and

potentially forfeits any upside potential In the process it gives its ldquopartnerrdquo proprietary

information they can use to trade for solely their own benefit

INCREASING EFFECTIVENESS ThROUGh IMPROVED INFRASTRUCTURE

The effects of steady growth were slowly compromising the risk management effectiveness of a major North American crude and distillates marketertrader Reporting timeliness was eroding error rates were climbing and managementrsquos confidence in risk control was decreasing prompting a full review of the organizational design and processes This revealed that the risk control function (middle office) was still performing much of the trade reconciliation that trade details were transmitted by email and that most reporting was done through spreadsheets

Several straightforward organizational shifts and systems enhancements eliminated a number of bottlenecks and enabled risk reporting down to the cargo level by desk and counterparty

ANALYTICS

The third pillar of a commodity risk management program is Analytics Organizations

cannot assess their financial exposure to commodity price swings without robust analytical

tools These analytics (or ldquomodelingrdquo) platforms support decision making at every level

mdash by helping managers model the future paths of commodity prices conduct stress- and

scenario-testing and evaluate and optimize risk-return profiles using a range of price-risk

management strategies however analytical capabilities vary dramatically from firm to firm

Companies should take a sequential approach to developing a robust analytics framework

that will support commodity risk management

Copyright copy 2011 Oliver Wyman 6

ThINK LIKE A TRADER

Trading is viewed as a necessary evil by some corporations given the connotations of excessive speculation and risk-taking with derivatives (in this case commodity futures contracts) A recent review of annual reports reveals that many companies disclosing commodity positions caution that these are strictly for hedging purposes and that the company ldquodoes not hold financial derivative positions for the purposes of tradingrdquo

Whether they realize it or not many companies are in fact speculating on commodity pricesmdashon both the cost and revenue sides Procurement officers are tasked with getting the best price on purchases for the company and its business units So the procurement staff commonly enters into forward fixed-price arrangements to guarantee both supply and pricing Just like traders they have thus made a bet on future prices and concluded price risk management with that view in mind

Companies need to accept that the days of a ldquoset it and forget itrdquo approach to risk management are over That approach was fine when volatility was low and prices increased gradually over time Today companies need to empower designated executives to think like traders ldquoIs this a good or bad price Should I buy more or run down inventoryrdquo Since most companies arenrsquot traders by nature they need to create a trading playbook that is integrated with their overall game plans

First a company must understand its commodity risk profile This helps the organization

assess its net exposure to commodity pricesmdashand their inevitable volatilitymdashacross

business and customer segments Detailed analysis also allows the organization to then

develop long-term strategies to mitigate commodity-related risks

A commodity risk profile provides a common understanding for senior management and

a fact-based foundation for evaluating the effectiveness of risk-mitigation actions With

this knowledge management can determine if the companyrsquos current commodity risk

exposure is within its risk tolerance and whether it has communicated these expectations

to stakeholders This analysis also helps the management team evaluate different risk

management strategies It promotes risk mitigation at the portfolio level which helps

reveal any risks lurking in individual business units or departments In short this analysis

will allow the company to optimize risk-return positioning

ThE NET ExPOSURE ChALLENGE

Determining true net exposure requires consideration of several factors gross commodity exposure existing risk mitigations foreign exchange flows demand sensitivity and the interactions among these This can be challenging for an enterprise with wide-spread operations or multiple product lines Typically each factorrsquos net impact at the corporate level is first assessed and any offsets across factors can then be accounted for

Copyright copy 2011 Oliver Wyman 7

To manage commodity price risks in ways that are consistent with broader corporate

objectives companies need a robust set of analytic tools to calculate the current exposure

Exhibit 4 offers a six-step analytical approach that companies can use to determine the

effect of commodity price risks and mitigation efforts on key financial metrics

The first step in the analytical framework requires management to build a forecast

of commodity prices using simulations or other techniques The company should

complement these forecasts with an analysis of alternative price outcomes based on ldquostress

eventsrdquo to understand fully how prices may evolve

In the second step the management team estimates the volume of future commodity

purchases across the enterprise In Step 3 this demand forecast is combined with

price projections to determine the firmrsquos gross commodity exposure In Step 4 the risk

management strategies already in place are identified Then in Step 5 these are applied

against the gross exposure to generate a ldquonet commodity exposurerdquo for the enterprise The

process ends with Step 6 the creation of a holistic company-wide risk profile ndash with the

sensitivities in price projections identified in Step 1 used to explore the potential impact on

EBITDA debt covenants and other financial metrics

Copyright copy 2011 Oliver Wyman 8

ExhIBIT 4 CREATING A hOLISTIC COMMODITY RISK PROFILE

bull Use analytic engines to simulate potential commodity price pathways (data driven)

bull Incorporate market context and paradigm shiftsscenario analysis ( judgment driven)

bull Integrates historical patterns market intelligence and fundamental analysis

bull Unifies disparate views of expected and highlow commodity price scenarios across organization

bull Incorporates interrelationships and correlations between commodities and currencies

STEP ANALYSIS BENEFITS

bull Centralizes commodity requirements across business units and geographies

bull Enables testing of alternative commodity purchase requirements using price elasticity analysis

bull Determine expected commodity purchase volume based on sales expectations

bull Gives context of expected commodity exposure compared to PampL and other risks

bull Accounts for ldquonatural hedgesrdquo in the portfolio

bull Shows shifts due to changes in business mix commodity price expectations

bull Calculate expected commodity exposure (eg price multiplied by volume)

bull Brings together and coordinates dierent functions of the organization (eg Strategy Procurement Treasury) and geographies

bull Sets understanding of risk management options for key commodity exposures

bull Define options for managing commodity price risk

bull Centralize risk management options undertaken across organization

bull Helps management assess the eectiveness of the risk management portfolio vs desired exposure

bull Provides understanding of how commodity price risk flows through the organization

bull Calculate exposure after incorporating current risk management portfolio

bull Enables objective evaluation and comparison of a range of risk management strategies

bull Promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at individual business unit or department level

bull Provides view of the earnings impact from commodity prices at dierent levels of probability

bull Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

COMMODITY PRICEPROJECTIONS

SALES PRICINGANDPURCHASE VOLUMES

GROSS EXPOSURE

RISK MANAGEMENTPORTFOLIO

NET EXPOSURE

HOLISTIC COMMODITYRISK PROFILE

Source Oliver Wyman

Copyright copy 2011 Oliver Wyman 9

IMPROVING COMPETIVENESS WITh A STRATEGIC RESPONSE TO COMMODITY VOLATILITY

A global food ingredient processor was losing sales to much smaller competitors solely because those firms were much more responsive to the highly volatile product price By analyzing the impacts of its forward sales process and market price uncertainty a new strategy was developed to migrate customers to shorter term contracts and to begin linking prices to market indices ndash which immediately improved the firmrsquos competitive positioning

STRATEGIES FOR MANAGING COMMODITY RISK

With a well-defined risk profile and an understanding of its ability to manage risk a

company can build a plan that matches its net exposure to commodities with its tolerance

for risk To manage commodity prices in the short term companies generally have three

tools at their disposal

bull Product pricingmdash identifying customer segments where the company has the ability to

raise prices or create pricing structures that mitigate risk

bull Procurement contract structuringmdashdeveloping innovative risk-sharing contracts with

suppliers

bull Financial hedgingmdashusing financial instruments for hedging to reduce overall

risk exposure

The effectiveness of these short-term strategies depends on the size of the organizationrsquos

exposure to a given commodity mdash and the commodity itself For example financial hedging

works best in commodity markets that are liquid (eg energy products such as crude oil and

natural gas or agricultural products such as wheat and corn) Meanwhile passing higher

commodity costs to customers through price increases is often ineffective in competitive

markets such as consumer products however when commodity cost increases are

significant and widespread pass-through pricing might be more viable Some companies

have taken creative approaches to raising prices For instance a number of consumer

products companies have reduced the volume of productmdashwhile keeping the same

package sizemdashto maintain margins in the face of higher commodity costs Other firms have

substituted cheaper ingredients to lower their net product costs

Over the long term volatility in commodity prices affects the behaviors of consumers

companies and their suppliers In response some companiesmdashand even countries--have

embedded commodity risk strategies into their long-term strategic plans

Copyright copy 2011 Oliver Wyman 10

CONCLUSION

Large and sustained commodity price swings are reshaping whole industries This means

that commodity risk management can no longer be considered the sole responsibility of

the procurement or finance staff With rising commodity prices affecting both the short-

term earnings and long-term strategies it is imperative that C-level executives develop a

deeper understanding of how to mitigate these risks Developing a structured commodity

risk management program built around the components outlined here is crucial Given

the new realities of higher prices and more volatility in commodities organizations must

integrate commodity risk management into their day-to-day operations They also must

build it into their long-term strategies to ensure the viability of the firm itself

ABOUT THE AUTHORS

MICHAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman

Michael J Denton PhD is a New York based Partner in Oliver Wymanrsquos Global Risk amp Trading practice with specialized experience in

energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market

risk modeling portfolio dynamics and risk-based decision frameworks Recent projects have focused on due diligence evaluations

competitive contracting and counterparty credit risk mitigation

ALEX WITTENBERG

Partner Global Risk amp Trading Oliver Wyman

Alex is the Managing Partner of Oliver Wymanrsquos Global Risk Center and has over 20 years of cross-industry experience in risk management

advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision-making and financial performance designing

risk governance for Boards and Management and developing corporate risk monitoring mitigation and transfer frameworks

Copyright copy 2011 Oliver Wyman 11

Copyright copy 2011 Oliver Wyman All rights reserved This report may not be reproduced or redistributed in whole or in part without the written permission of Oliver Wyman and Oliver Wyman accepts no liability whatsoever for the actions of third parties in this respect

The information and opinions in this report were prepared by Oliver Wyman

This report is not a substitute for tailored professional advice on how a specific financial institution should execute its strategy This report is not investment advice and should not be relied on for such advice or as a substitute for consultation with professional accountants tax legal or financial advisers Oliver Wyman has made every effort to use reliable up-to-date and comprehensive information and analysis but all information is provided without warranty of any kind express or implied Oliver Wyman disclaims any responsibility to update the information or conclusions in this report Oliver Wyman accepts no liability for any loss arising from any action taken or refrained from as a result of information contained in this report or any reports or sources of information referred to herein or for any consequential special or similar damages even if advised of the possibility of such damages

This report may not be sold without the written consent of Oliver Wyman

wwwoliverwymancom

Oliver Wyman is a leading global management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development Oliver Wymanrsquos Global Risk Center is dedicated to analyzing increasingly complex risks that are reshaping industries governments and societies Its mission is to assist decision makers in addressing risks by combining Oliver Wymanrsquos rigorous analytical approach to risk management with leading thinking from professional associations non-governmental organizations and academic institutions For further information please visit wwwoliverwymancomglobalriskcenter

The Association for Financial Professionals (AFP) headquartered outside Washington DC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFP is the daily resource for the finance profession

For more information please contact

MIChAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman +16463648423 michaeldentonoliverwymancom

ALEx WITTENBERG

Partner Global Risk amp Trading Oliver Wyman +16463648440 alexwittenbergoliverwymancom

BRIAN T KALISh

Director Finance Practice Lead +13019616564 bkalishafponlineorg

Oliver Wyman
File Attachment
Commodity volatilitypdf

intRoduCtion

given the current and future trends in commodity prices and volatility every company must

better understand its true commodity exposure Companies can often be squeezed by

rising and volatile commodity input prices that cannot be passed along to customers in

their entirety A commodity risk management program can help not all organizations can

or should adopt the sophisticated mechanisms of a pure commodity business however

most organizations particularly those in the middle of the value chain can improve their

commodity risk analytics

exhibit 1 CompAnies ARe ChAllenged by Rising Commodity volAtility

Output prices

bull Competitive pressuresbull Limited ability to pass

through higher costs

COMPANY

bull Understand current forecasts (starting position and price curve)

bull Trace impact of price volatility to earnings

bull Align commodity risk management program strategy with corporate objectives

Commodity risk management program strategy instruments etc

Input prices

bull Increasing volatilitybull Increasing absolute pricebull Breakdown in correlations

the starting point is to understand the companyrsquos holistic commodity risk profile using

analytics and modeling tools A holistic commodity risk profile helps the organization assess

its individual and net exposure to commodity pricesmdashand the inevitable volatilitymdashacross

business and customer segments on a forward-looking basis the profile provides a common

understanding for senior management and a ldquofact-basedrdquo foundation for evaluating the

effectiveness of current risk-mitigation actions and alternative risk management strategies

With this knowledge management teams can determine if current commodity risk exposure

is within the companyrsquos risk tolerance and communicate these expectations to stakeholders

it also helps to promote risk mitigation at the portfolio level by identifying the most

important drivers of overall risk and ensuring the capture of any offsetting risks that may be

present in short this analysis will allow the company to optimize risk-return positioning

this In Practice Guide provides an overview of a stepwise approach and analytic processes

necessary to develop an organizationrsquos net commodity exposure

Copyright copy 2012 oliver Wyman 3

six steps to deteRmining Commodity exposuRe And impACts

exhibit 2 provides an overview of a six-step analytical approach to determine the impact of

commodity price risks on key financial metrics

exhibit 2 six steps to CReAte A holistiC Commodity Risk pRoFile

6 HOLISTIC COMMODITY RISK PROFILE

5 NET EXPOSURE

4 RISK MANAGEMENT

PORTFOLIO

3 GROSS EXPOSURE

2 SALES PRICING AND COMMODITY

PURCHASE VOLUMES

1 COMMODITY PRICE

PROJECTIONS

STEPS

ANALYSISUse analytic engines to simulate potential commodity price pathways (data driven)

Incorporate market context and paradigm shifts scenario analysis (judgement driven)

Calculate expected commodity exposure (ie price multiplied by volume)

Define options for managing commodity price risk

Centralize risk management options undertaken across organization

Calculate exposure after incorporating current risk management portfolio

Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

Determine expected commodity purchase volume based on sales expectations

Copyright copy 2012 oliver Wyman 4

step 1 Commodity pRiCe pRojeCtions

CoRe AnAlysis user-defined baseline commodity prices and volatilities for a predefined set of commodity

inputs are incorporated into a standard simulation process to generate a distribution of

potential future price paths for each commodity

outputs A distribution of terminal price values for each commodity and time horizon

beneFits bull integrates historical patterns market intelligence and fundamental analysis

bull unifies views of commodity price scenarios across the organization

bull incorporates interrelationships and correlations between commodities and other price

risk factors (eg currencies)

the first step in the analytical framework requires

management to build assumptions for commodity

prices in the future using simulation or other techniques

the management team should complement these

forecasts with an analysis of alternative price outcomes

based on ldquostress eventsrdquo to understand fully how prices

may evolve

A simple price simulation model is presented in the

attached workbook (accessed through the pushpin

icon) the inputs for these simulations are located on

the tab labeled ldquointerfacerdquo under the heading ldquoprice

and volatilityrdquo the user defines base case prices and

volatilities for each commodity and forward period

(ie 1st quarter 2nd quarter and so on) the simulated

terminal value prices or outcomes for each commodity

and time horizon are shown on the tab labeled ldquoCmdy

price projrdquo each of these values reflects possible future

outcomes for commodity prices based on three factors

mean price volatility and time

the outputs of the simulations for each commodity are

summarized on the ldquointerfacerdquo page in the ldquoCommodity

price projectionsrdquo section in each case 5th and 95th

percentile outcomes are shown along with the baseline

price scenario over the course of the time period to

provide a richer understanding of possible outcomes

versus a single static forecast not surprisingly the

uncertainty of price forecasts grows with the increasing

time horizon

Copyright copy 2012 oliver Wyman 5

ReadMe

OverviewThe workbook has been prepared as a supplement to the In Practice Guide Six Steps to Assess Commodity Risk Exposure prepared by Oliver Wyman for AFP members and available at wwwafponlineorg The purpose of this workbook is to provide a simple tool for illustration purposes only to better understand and quantify commodity risk impacts on earmings The example focuses on a corporation which utilizes key commodities including natural gas diesel fuel sugar electricity and aluminum The components of the workbook include a primary Interface worksheet as well as support worksheets which house the calculations used to generate outputs on the Interface sheet Note that all user inputs and outputs are exclusively contained in the Interface sheet - all other sheets are for reference only Cells in white in the User Inputs block represent input cells (prices volatilities and hedge designation) Any changes in outputs are generated by using the Calculate button The Interface worksheet receives inputs and provides outputs for a probabilistic earnings forecast model and illustrates the impacts of commodity prices and volatilities on the earnings profile In particular each of the outputs reflects one of the steps in the 6 step process Outputs include(1) Commodity price projections - User-defined baseline commodity prices and volatilities for a pre-defined set of commodity inputs are incorporated into a standard simulation process to generate a distribution of potential future price paths for each commodity(2) Estimated commodity volumes - A predefined set of commodity volumes reflecting commodity demand over time(3) Gross commodity exposure - User-defined baseline prices are combined with simulated price paths and volumes to generate commodity exposure - measured as the difference between the 95th percentile worst case outcome and the baseline expected outcome (4) Net commodity exposure - Gross commodity exposure is adjusted for any financial or physical hedges for each of the commodities Hedges are assumed to lock in the commodity at the baseline expected price Price correlations between commodities are taken into account(5) Holistic commodity risk profile - Commodity exposures are combined with predefined estimates for revenues and other fixed costsSGA to illustrate both positive and negative impacts on the earnings profileKey assumptionsKey assumptions in the model include the following - A GBM process is used to simulate all of the commodity price paths - Correlations are predefined and fixed resulting in a static set of shocks used in the simulation process- User defined volumes and revenues are static- User defined prices and volatiliities are used as baseline assumptions for each quarter

Interface

Interface

REF
1

Cmdy Price Proj

Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
124418764137
1367693617635
2648580611483
786217932233
803244438443
5605736599794
124418764137
4238042982159
1367693617635
1589462370676
2648580611483
803244438443
786217932233
0
803244438443

Correlations

5
95
Mean
Sugar
01107087317
03452715728
02
00854552129
04050827214
021
00695646039
04616729635
022
0061034605
05340468686
023

Sales Pricing Volumes

Sugar
Natural gas
Electricity
Diesel
Aluminum

Gross Exposure

REF
EPS-at-risk
1

Risk Mgmt Portfolio

REF
EBITDA
1

Simulation Net Exposure

Diesel
Electricity
Natural gas
Crude oil
Price change
EPS impact
Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
1912572265773
1138117311849
1319658025171
11406289411
1162397994146
3734236225276
1912572265773
2596118913427
1138117311849
1276460888256
1319658025171
1162397994146
11406289411
0
1162397994146
5
95
Mean
Natural Gas
40688610903
79154680028
57310928297
32742030186
84855775584
57310928297
28887025303
92658501718
57310928297
26679947564
10281600624
57310928297

Net Exposure

5
95
Mean
Electricity
344141498094
1003995388957
622705433902
264877650618
1259146360173
622705433902
219723962528
1384358671012
6227
15528763279
1491896493046
622705433902

Fin Statement

5
95
Mean
Diesel
16508189205
29538139317
22444582473
14672697038
31955148416
22444582473
13074767002
36018057848
22444582473
11953891033
37258114813
22444582473

Price shock contuity

5
95
Mean
Aluminum
134769028369
254013105136
1878
119721652401
284279599425
187771063122
106347176349
316018108859
187771063122
95839609124
346925945325
187771063122

Graph support

Sugar
Natural gas
Electricity
Diesel
Aluminum
128
96
1
16
16
134
101
1
168
17
141
106
1
176
18
150
111
1
185
19
95ile
5ile
Earnings statement
-161353539101
1326569803377
611516334878
-421367106558
1798147036403
760774397443
-642177203637
2144399264928
918591594906
-1050874848209
2521161989213
1080153466317
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Correlations among commodity prices dictate the way in which commodity prices move together and are used to develop commodity price projections via simulation
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Commodity volumes may be determined by reviewing sales and demand planning forecasts Volumes may be aggregated when sourced from different business divisions or regions to arrive at a corporate-wide volume estimate
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Oliver Wyman
File Attachment
AFP-OW Commodity Risk Calculatorxls

step 2 sAles pRiCing And puRChAsing volumes

CoRe AnAlysis user-defined commodity volumes for the predefined set of commodities are coupled

with the distribution of price outcomes to support the determination of gross and net

exposures to commodities sales volumes and prices are incorporated into the pro forma

financial statement to understand earnings impacts from commodity exposures

outputs A predefined set of commodity volumes reflecting commodity demand over time

beneFits bull Centralizes commodity requirements across business units and geographies

bull enables testing of alternative commodity purchase requirements using price

elasticity analysis

in the second step the management team must

estimate the volume of future commodity purchases

across the enterprise the estimated volumes for inputs

are going to be directly linked to sales forecasts and

thus may be derived from product demand profiles

While the current model focuses on price uncertainty

it is important to recognize the presence of volume

uncertainty as well the static volumes are estimated

and then entered by the user on the tab labeled

ldquointerfacerdquo under the heading ldquoCommodity volumesrdquo

Assumptions for product sales volumes and prices are

also entered on the ldquointerfacerdquo tab for later use in the

holistic commodity risk profile assessment

step 3 gRoss exposuRe

CoRe AnAlysis user-defined baseline prices are combined with simulated price paths and volumes to

generate commodity exposure as measured by the difference between the 95th percentile

worst case outcome and the baseline expected outcome

outputs A firmrsquos gross commodity exposure given commodity price and volume

beneFits bull gives context of expected commodity exposure in comparison to pampl and other risks

bull shows changes over time due to either changes in business mix or changes in

commodity price expectations

in the third step the expected gross commodity

exposure is determined the 95th percentile worst case

commodity costs located on the tab labeled ldquogross

exposurerdquo are found by multiplying each commodity

volume by price gross exposure is determined by

summing the difference between the baseline cost and

the simulated 95th percentile worst case cost for each

individual commodity exposure Alternative percentiles

may be selected using the ldquoexposure risk levelrdquo input

value on the ldquointerfacerdquo tab

Copyright copy 2012 oliver Wyman 6

step 4 Risk mAnAgement poRtFolio

CoRe AnAlysis Risk management strategies currently in place are identified

outputs portfolio of existing risk management options (eg a set of commodity derivative hedges)

beneFits bull brings together and coordinates different functions of the organization (ie strategy

sales and marketing procurement treasury) and geographies

bull sets common understanding of risk management options for key commodity exposures

After determining gross commodity exposure the next

step in the process is to identify existing risk management

strategies the tab labeled ldquoRisk mgmt portfoliordquo has

a predefined list of risk management options available

which limit the management decision to financial

hedges that are assumed to lock-in the commodity at the

baseline expected price on the tab labeled ldquointerfacerdquo

under the heading ldquohedge selectionrdquo the user selects

ldquoyesrdquo or ldquonordquo for each commodity depending on whether

the firm plans to hedge that particular commodity it

is important to note that management of commodity

price risk is not limited only to financial hedging rather

it typically involves a combination of four broad levers

ndash each (or a combination thereof) may be effective as

illustrated in exhibit 3

besides recognizing current risk management

strategies this step also alerts management of other

potential risk mitigation options that may be available to

manage key commodities exposures

Copyright copy 2012 oliver Wyman 7

exhibit 3 Risk mAnAgement options

a Product Pricing (incl ldquoPass-throughrdquo)

b Procurement contracts

c Financial hedging

d Value chain integration long-term strategies

oVerView bull use market pricing

power to recover

change in input costs ndash

revenue management

bull balancesharetransfer

risk on purchases ndash

cost management

bull transfer risk to third

party through markets

structured deals

(Reinsurers also providing

capacity here)

bull up or downstream

integration eg

sourcing raw materials

retail trading

examPle strategies

bull employ cost pass-through

mechanisms (across

all or a portion of the

product portfolio)

bull Align price risk transfer

mechanisms with sales and

purchase contracts

bull include contract

mechanisms that mitigate

risk (eg price ceilings

floors price bands knock-

inknock-out clauses)

bull hedge price exposure

with swaps to set the price

or options or collars to

limit downside

bull Cross-hedge exposure

with contracts with

strong correlation to

underlying exposure

bull Acquire upstream company

to ensure supply and

diversify risk exposure

bull identify drivers that

contribute greatest

percentage to overall risk

Pros bull Raw materials costs fully

or partially passed through

to customers

bull stabilized margins and

earnings certainty

bull protection against price

moves and volatility

bull transition risk back

to supplier under

certain structures

bull protection against

feed stock price moves

and volatility

bull no impact on product

pricingsupplier contracts

bull holistic approach to cost

and margin management

bull diversification of

risk exposure

cons bull Customers may substitute

products or move to

lower priced competitor

brands in response to

product price

bull suppliers demand

premium (higher price)

for risk sharing

bull need to find

receptive suppliers

bull liquidity premium and

market non-availability

for many commodities

bull basis risk from

cross-hedging

bull Requires appropriate

capital and expertise

for joint ventures

or acquisitions

bull increased organizational

complexity

Copyright copy 2012 oliver Wyman 8

step 5 net exposuRe

CoRe AnAlysis predefined financial hedges and price correlations between commodities are applied to

gross commodity exposure to determine net commodity exposure for the enterprise

outputs A firmrsquos net commodity exposure after incorporating its current risk management portfolio

beneFits bull Accounts for natural hedges

bull provides management with an understanding of the effectiveness of the risk

management portfolio vs desired exposure

bull provides broad understanding of how commodity price risk flows through

the organization

While calculating net exposure management must also

take into account the correlation between commodity

prices price movements of one commodity may be related

to the price movement of other commodities thereby

increasing or decreasing exposure depending on the

relative strength of the relationship to account for this the

net exposure of the portfolio must be determined on the

tab labeled ldquosimulation net exposurerdquo the distribution

of terminal price values for each commodity and time

period (eg 1st quarter 2nd quarter etc) are multiplied

against volume for that same time period A distribution

of net exposures is generated and 5th and 95th percentile

outcomes for the portfolio series are selected

A companyrsquos net exposure to commodity price volatility is

shown on the tab labeled ldquonet exposurerdquo the predefined

volume levels are multiplied by the new correlated prices

to determine the 95th percentile worst case net exposure

if the company is employing a hedging strategy for a

commodity then it has no exposure for that particular

commodity since hedges are assumed to lock-in the

commodity at the baseline expected price

step 6 holistiC Commodity Risk pRoFile

CoRe AnAlysis Commodity exposures and price projections are combined with predefined estimates for

revenues and other fixed costssgA to illustrate both positive and negative impacts on the

earnings profile

outputs pro forma financial statement with predefined revenue and fixed costsgA reflecting the

impact of price variations and hedging on earnings

beneFits bull enables objective evaluation and comparison of a wide variety of risk

management strategies

bull promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at an

individual bu level

bull gives management a view of earnings impact from commodity prices at different levels

of probability

the process of determining net commodity exposure

ends with the creation of a holistic company-wide

risk profile ndash with the sensitivities in price projections

identified in step 1 used to explore the potential impact

on ebitdA debt covenants and other financial metrics

this impact is shown on the tab labeled lsquointerfacersquo in the

section called ldquoholistic commodity profile and impact

on earningsrdquo

Copyright copy 2012 oliver Wyman 9

ConClusion

large and sustained commodity price swings are quickly reshaping industries and the

businesses that exist within them in this environment commodity risk management must

be firmly integrated into the strategy of the company and structured under a commodity risk

management framework

executives need to recognize that their strategies may have to change to accommodate how

volatile commodity prices are redefining their competitive landscape and develop a process

to manage them across functions decisions around commodity risk management must be

based on a forward-looking view this process can include a series of tough questions about

the sustainability of business models such as

bull Will a perception of scarcity resulting from sustained volatile commodity prices impact

that commodityrsquos availability

bull Will changing commodity correlations invalidate diversification plays to hedge against

other commodities

bull Will consumers shift to cheaper private label alternatives or change their lifestyles to

avoid expenses introduced by higher commodity prices

the companies who are first to define their risk management metrics to include extreme

prices and anticipate how they can best benefit from them will have a significant

competitive advantage

Copyright copy 2012 oliver Wyman 10

Copyright copy 2012 oliver Wyman 11

For more information please contact

michael denton Partner global risk and trading oliver wyman

+16463648440 michaeldentonoliverwymancom

michael denton is a new york-based partner in oliver Wymanrsquos global Risk amp trading practice with specialized experience in energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market risk modelling portfolio dynamics and risk-based decision frameworks

alex wittenberg Partner global risk and trading oliver wyman

+16463648440 alexwittenbergoliverwymancom

Alex Wittenberg has over 20 years of cross-industry experience in risk management advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision making and financial performance designing risk governance for boards and management and developing corporate risk monitoring mitigation and transfer frameworks

brian t Kalish director Finance Practice lead

+13019616564 bkalishafponlineorg

brian t kalish is the director and Finance practice lead of the Association of Financial professionals he is responsible for the Finance practice which includes Corporate Finance Risk management Capital markets investor Relations Financial planning amp Analysis Accounting and Financial Reporting

oliveR WymAn And the AssoCiAtion FoR FinAnCiAl pRoFessionAls the FinAnCiAl insights seRies

this is part of series of papers by international management consulting firm oliver Wyman and the Association for Financial professionals that address critical issues for finance professionals oliver Wyman and AFp welcome your thoughts on the ideas presented in this paper if you would like to provide feedback or be alerted when we schedule events or other activities for corporate finance professionals please contact one of the individuals listed below

oliver Wyman is an international management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development

the Association for Financial professionals (AFp) headquartered outside Washington dC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFp is the daily resource for the finance profession

wwwoliverwymancom

Copyright copy oliver Wyman All rights reserved

AuthoRs

michael denton Partner global risk and trading oliver wyman

alex wittenberg Partner global risk and trading oliver wyman

brian t Kalish director Finance Practice lead

Gross Exposure graph support (stacked graph and waterfall)
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 5606 4238 1589 803 00
Grey 1244 1368 2649 786 803
Sugar 1244 186 261 341 456 1244
Natural gas 1368 210 278 375 505 1368
Electricity 2649 381 636 762 869 2649
Diesel 786 113 160 239 274 786
Aluminum 803 106 164 231 302 803
Commodities volume graph support
Q1 Q2 Q3 Q4
Sugar 12800 13400 14100 15000
Natural gas 960 1010 1060 1110
Electricity 100 100 100 100
Diesel 1600 1680 1760 1850
Aluminum 160 170 180 190
Net exposure graph support
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 3734 2596 1276 1162 00
Grey 1913 1138 1320 114 1162
Sugar 1913 207 404 530 771 1913
Natural gas 1138 168 250 348 372 1138
Electricity 1320 221 266 331 502 1320
Diesel 114 26 39 12 38 114
Aluminum 1162 151 223 340 448 1162
Financial statement graph support
Cost (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 463 686 840 1116 3105
Volume (lbs) 1280 1340 1410 1500 463 686 840 1116 3105 Unhedged
Price 036 051 060 074
Natural gas 718 829 956 1008 718 829 956 1008 3511
Volume 96 101 106 111 718 829 956 1008 3511 Unhedged
Price 75 82 90 91
Electricity 844 888 953 1125 844 888 953 1125 3810
Volume 10 10 10 10 844 888 953 1125 3810 Unhedged
Price 844 888 953 1125
Diesel 385 416 407 453 385 416 407 453 1660
Volume 160 168 176 185 385 416 407 453 1660 Unhedged
Price 24 25 23 24
Aluminum 451 543 678 805 451 543 678 805 2477
Volume 16 17 18 19 451 543 678 805 2476841735006 Unhedged
Price 282 319 377 424
Total 2861 3361 3834 4507 2861 3361 3834 4507 14564
2861 3361 3834 4507 14563772697504 Unhedged
Cost (5)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 179 142 141 119 179 142 141 119 581
Volume (lbs) 1280 1340 1410 1500 179 142 141 119 581 Unhedged
Price 014 011 010 008
Natural gas 347 283 251 241 347 283 251 241 1123
Volume 96 101 106 111 347 283 251 241 1123 Unhedged
Price 36 28 24 22
Electricity 332 223 176 138 332 223 176 138 868
Volume 10 10 10 10 332 223 176 138 868 Unhedged
Price 332 223 176 138
Diesel 275 264 244 231 275 264 244 231 1015
Volume 160 168 176 185 275 264 244 231 1015 Unhedged
Price 17 16 14 12
Aluminum 241 230 236 205 241 230 236 205 911
Volume 16 17 18 19 241 230 236 205 911127595797 Unhedged
Price 150 135 131 108
Total 1373 1142 1048 935 1373 1142 1048 935 4498
95ile 5ile
Sales 480 504 529 556 480 504 529 556
Fixed Costs 120 120 120 120 120 120 120 120
Variable Costs 286 336 383 451 137 114 105 93
General amp Admin Exp 90 90 90 90 90 90 90 90
EBITDA (16) (42) (64) (105) 133 180 214 252
Title To quickly check to make sure prices are still the same filter the prices on the tab proj 3 to go in order 1 to 100 and this should match
[Optional comments]
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
Earnings statement
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015
000
Cost of Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($lbs) 2560 2814 3102 3450 11926
Volume (lbs) 12800 13400 14100 15000
Price 020 021 022 023
000
Natural gas ($Kcf) - Henry Hub 5502 5788 6075 6362 23727
Volume 960 1010 1060 1110
Price 573 573 573 573
000
Electricity ($MWh) - NY ISO 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($gal) - NY Harbour 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
000
General amp Admin Exp 9000 9000 9000 9000 36000
000
EBITDA 6115 7608 9186 10802 33710
Impact of hedging
all figures in millions (except prices)
Futures Used
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
Net exposure (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 207 404 530 771 1913
Volume (lbs) 1280 1340 1410 1500
Price 036 051 060 074
00 00 00 00 00
Natural gas 718 829 956 1008 168 250 348 372 1138
Volume 96 101 106 111
Price 75 82 90 91
00 00 00 00 00
Electricity 844 888 953 1125 221 266 331 502 1320
Volume 10 10 10 10
Price 844 888 953 1125
00 00 00 00 00
Diesel 385 416 407 453 26 39 12 38 114
Volume 160 168 176 185
Price 24 25 23 24
00 00 00 00 00
Aluminum 451 543 678 805 151 223 340 448 1162
Volume 16 17 18 19
Price 282 319 377 424
Total 2861 3361 3834 4507 773 1182 1561 2131 5647
Correlated Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum Total Net Exposure
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Full-Year
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Volume 12800 13400 14100 15000 960 1010 1060 1110 100 100 100 100 1600 1680 1760 1850 160 170 180 190
Simulated
5 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 48715
95 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145722
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
Mean
Net Exposure Net Portfolio Exposure
32380 47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739 32380
33948 42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789 33948
39948 55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685 39948
43542 60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103 43542
44977 68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 44977
48911 50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956 48911
49824 97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168 49824
52614 9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173 52614
54247 89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834 54247
54467 29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518 54467
56081 37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959 56081
56950 66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358 56950
58740 44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783 58740
59233 63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890 59233
60239 11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751 60239
60316 3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213 60316
61664 56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573 61664
65269 4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321 65269
65273 92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204 65273
65764 69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759 65764
65876 93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008 65876
66078 87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886 66078
66116 74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632 66116
66562 28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686 66562
66781 5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205 66781
68244 27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291 68244
69290 94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559 69290
69701 32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209 69701
70796 10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158 70796
71222 62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991 71222
71261 45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691 71261
72702 91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228 72702
73734 20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550 73734
73801 52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806 73801
74108 88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414 74108
74317 1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024 74317
74660 22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160 74660
74808 31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368 74808
74998 82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171 74998
75350 8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146 75350
75850 85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158 75850
76270 2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409 76270
78037 30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375 78037
78226 23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103 78226
81022 96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662 81022
82068 99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748 82068
82643 35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858 82643
87060 14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447 87060
87614 83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068 87614
89614 46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928 89614
90297 100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267 90297
91492 49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762 91492
92285 79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150 92285
92517 80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079 92517
92520 58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466 92520
93297 38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511 93297
93492 84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782 93492
94010 72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702 94010
95355 25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982 95355
95981 51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310 95981
96590 70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430 96590
96659 59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987 96659
98984 39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335 98984
99473 64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881 99473
100438 18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643 100438
104925 26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805 104925
106683 71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274 106683
107046 48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657 107046
108929 75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922 108929
109789 17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544 109789
109940 98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393 109940
111963 61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121 111963
113140 41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558 113140
114475 15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927 114475
115420 76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479 115420
115470 12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839 115470
115543 43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938 115543
115599 16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233 115599
115628 95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056 115628
115695 13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336 115695
116747 78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058 116747
118794 33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808 118794
120798 67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960 120798
122134 7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729 122134
123516 73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263 123516
125606 90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639 125606
126928 54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417 126928
128846 77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264 128846
128878 6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513 128878
130807 34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239 130807
140398 19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517 140398
142414 81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603 142414
143917 53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292 143917
145005 65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126 145005
145638 36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145638
147330 40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883 147330
149955 86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437 149955
167385 24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467 167385
178469 57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836 178469
195830 21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430 195830
Risk management portfolio
Futures Description
Sugar Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Natural gas Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Electricity Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Diesel Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Aluminum Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Gross exposure calculations
Gross Exposure
all figures in millions (except prices) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 442 543 651 801 186 261 341 456 1244
Volume (lbs) 1280 1340 1410 1500
Price 035 041 046 053
00 00 00 00 00
Natural gas 760 857 982 1141 210 278 375 505 1368
Volume (MMBTU) 96 101 106 111
Price 792 849 927 1028
00 00 00 00 00
Electricity 1004 1259 1384 1492 381 636 762 869 2649
Volume (MWh) 10 10 10 10
Price 1004 1259 1384 1492
00 00 00 00 00
Diesel 473 537 634 689 113 160 239 274 786
Volume (gal) 160 168 176 185
Price 295 320 360 373
00 00 00 00 00
Aluminum 406 483 569 659 106 164 231 302 803
Volume (tons) 16 17 18 19
Price 2540 2843 3160 3469
Total 3085 3679 4220 4783 996 1500 1947 2407 6850
Sales Pricing and Volumes
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015 016
000
Cost of Commodity Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($) 2560 2814 3102 3450 11926
Volume (lb) 12800 13400 14100 15000
($lb) 020 021 022 023
000
Natural gas ($) 5502 5788 6075 6362 23727
Volume (MMBTU) 960 1010 1060 1110
Price ($MMBTU) 573 573 573 573
000
Electricity ($) 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($l) 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum ($) 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
Correlations
Correlated random shocks generated from correlation matrix
Correlations Sugar Natural gas Electricity Diesel Aluminum
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Sugar Q1 100 099 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q2 099 100 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q3 099 099 100 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q4 099 099 099 100 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Nat Gas Q1 065 065 065 065 100 099 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q2 065 065 065 065 099 100 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q3 065 065 065 065 099 099 100 099 094 094 094 094 086 086 086 086 069 069 069 069
Q4 065 065 065 065 099 099 099 100 094 094 094 094 086 086 086 086 069 069 069 069
Electricity Q1 055 055 055 055 094 094 094 094 100 099 099 099 082 082 082 082 071 071 071 071
Q2 055 055 055 055 094 094 094 094 099 100 099 099 082 082 082 082 071 071 071 071
Q3 055 055 055 055 094 094 094 094 099 099 100 099 082 082 082 082 071 071 071 071
Q4 055 055 055 055 094 094 094 094 099 099 099 100 082 082 082 082 071 071 071 071
Diesel Q1 047 047 047 047 086 086 086 086 082 082 082 082 100 099 099 099 066 066 066 066
Q2 047 047 047 047 086 086 086 086 082 082 082 082 099 100 099 099 066 066 066 066
Q3 047 047 047 047 086 086 086 086 082 082 082 082 099 099 100 099 066 066 066 066
Q4 047 047 047 047 086 086 086 086 082 082 082 082 099 099 099 100 066 066 066 066
Aluminum Q1 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 100 099 099 099
Q2 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 100 099 099
Q3 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 100 099
Q4 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 099 100
Column C C C C G G G G - 0 K K K - 0 O O O S S S S
Indirect C_Prices - 0 - 0
Correlated Sugar Natural gas Electricity Diesel Aluminum
N(01) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1 (108) (092) (082) (107) (047) (078) (057) (062) (015) (003) (008) (027) 041 052 041 022 052 051 036 039
2 031 (007) (006) (003) (015) (014) (012) (009) (033) (017) (021) (009) 005 005 026 (009) (087) (103) (079) (074)
3 (176) (167) (159) (155) (092) (079) (090) (098) (058) (061) (057) (069) (009) 018 007 002 (124) (101) (128) (121)
4 (134) (119) (112) (122) (046) (044) (043) (052) (066) (052) (037) (044) 016 009 (016) (003) (106) (116) (111) (094)
5 (075) (080) (049) (077) (063) (028) (057) (072) (045) (056) (051) (042) 011 010 021 032 (135) (127) (130) (123)
6 082 082 072 095 137 110 115 132 119 098 119 143 095 104 085 108 092 107 098 107
7 035 050 038 044 098 108 105 100 140 129 127 131 123 119 119 139 (024) (022) (013) (010)
8 (056) (044) (076) (066) (017) (033) (033) (045) (009) (007) 003 016 (091) (074) (102) (071) 078 068 080 058
9 (025) (015) (023) (027) (104) (127) (109) (105) (135) (150) (144) (166) (149) (148) (165) (151) (130) (126) (134) (131)
10 126 116 115 112 (120) (097) (107) (114) (054) (046) (052) (053) (107) (099) (125) (099) (117) (120) (125) (135)
11 (166) (182) (173) (174) (090) (083) (095) (089) (061) (070) (065) (060) (066) (048) (061) (084) 006 (013) (002) (006)
12 014 014 034 034 074 062 073 046 131 148 164 155 003 (003) 002 (035) 006 (003) 000 010
13 034 028 011 035 142 130 113 120 118 112 086 091 060 030 044 052 085 081 088 084
14 (048) (059) (063) (068) 033 026 020 050 021 017 019 023 001 (012) 009 005 089 097 075 099
15 079 091 090 091 083 082 073 081 096 086 093 081 107 112 115 096 047 038 030 024
16 058 071 034 059 116 117 122 103 064 084 084 092 081 080 068 108 093 092 082 070
17 (046) (041) (043) (048) 099 112 121 101 138 111 102 126 031 049 027 052 (019) (030) (033) (045)
18 030 052 025 024 060 078 068 059 073 093 069 070 031 033 026 030 (020) (037) (024) (026)
19 (034) (031) (025) (026) 146 114 124 151 166 173 175 161 121 116 108 097 222 227 201 212
20 (119) (138) (136) (114) (009) (009) 003 (012) (048) (021) (028) (040) 073 048 037 039 (028) (009) (003) (044)
21 092 081 085 085 236 228 257 241 229 231 230 239 259 264 269 263 098 082 075 097
22 047 052 031 042 (014) (030) (006) (016) (028) (027) (041) (031) (035) (040) (014) (031) (122) (127) (135) (134)
23 (043) (047) (046) (069) (021) (019) (014) (044) (005) 020 005 (006) (032) (025) (021) (012) 075 060 052 051
24 082 083 064 089 195 192 202 195 198 209 195 198 138 109 128 113 201 194 206 208
25 039 030 035 039 042 037 048 054 067 069 066 059 (050) (065) (051) (061) 032 038 040 033
26 130 157 113 138 057 052 036 048 076 085 063 076 (053) (058) (061) (052) 009 004 (012) 004
27 (063) (063) (067) (046) (041) (045) (053) (035) 012 015 004 (003) (140) (136) (152) (120) (116) (127) (101) (101)
28 (051) (067) (068) (058) (075) (063) (051) (068) (056) (050) (053) (068) (031) (025) (019) (053) (022) (005) (014) (018)
29 (165) (163) (176) (155) (115) (130) (143) (128) (065) (067) (081) (085) (128) (105) (126) (127) (058) (047) (031) (050)
30 (029) (031) (029) (032) (007) (016) (019) (003) 010 018 005 012 001 017 007 (007) (077) (089) (071) (081)
31 (040) (055) (058) (043) (004) (009) (010) (041) (007) (023) (027) (028) (065) (082) (086) (075) 078 068 065 089
32 123 098 102 105 (047) (059) (069) (079) (101) (106) (094) (097) (069) (074) (072) (089) (125) (131) (115) (122)
33 057 052 066 070 082 074 077 075 085 082 081 079 079 090 068 091 245 239 238 237
34 174 156 162 156 142 131 136 135 081 071 075 086 143 133 150 126 046 052 053 071
35 (121) (127) (111) (122) (004) (005) 018 (009) 051 059 046 032 (021) 003 (018) (008) 034 046 012 013
36 187 205 195 203 123 123 130 120 105 097 101 120 046 051 025 042 262 246 265 270
37 (019) (031) (029) (029) (106) (097) (106) (108) (103) (098) (084) (097) (090) (092) (107) (106) (199) (187) (190) (194)
38 080 080 081 069 018 036 (001) 014 (002) (022) (018) (017) 074 071 073 069 109 102 092 107
39 040 053 044 058 085 065 070 063 020 040 029 023 029 053 042 047 076 059 077 084
40 085 100 108 089 134 123 111 133 158 169 164 168 151 138 173 169 149 174 152 150
41 082 097 061 076 101 085 105 108 092 079 093 076 111 111 112 113 (045) (064) (044) (042)
42 (123) (134) (135) (147) (263) (271) (274) (254) (270) (276) (279) (268) (210) (223) (208) (227) (245) (244) (236) (255)
43 (016) (018) (018) (034) 100 092 094 115 122 134 110 112 115 109 102 111 027 024 035 026
44 (048) (052) (042) (035) (120) (122) (121) (119) (085) (098) (098) (095) (112) (115) (120) (132) (006) 005 001 (000)
45 069 067 059 054 (073) (073) (070) (067) (057) (073) (070) (080) (033) (026) (039) (057) (045) (040) (018) (017)
46 005 011 013 005 014 048 036 037 031 036 041 047 (043) (018) (052) (039) 030 037 050 024
47 (235) (256) (232) (222) (247) (231) (242) (221) (232) (219) (219) (235) (272) (277) (282) (278) (312) (301) (287) (275)
48 143 131 119 126 084 106 097 108 (004) 017 025 021 087 072 067 071 (004) 001 (000) (023)
49 (063) (045) (054) (035) 023 021 028 027 075 105 080 081 (048) (052) (049) (045) (006) 001 002 (004)
50 (055) (060) (066) (079) (129) (132) (138) (130) (154) (145) (127) (155) (157) (121) (133) (121) (205) (205) (217) (195)
51 039 028 044 031 052 056 043 052 030 025 046 023 035 034 022 042 079 079 067 081
52 (087) (075) (088) (067) (009) 002 (016) (002) 007 (011) (013) 000 (087) (077) (079) (081) (001) 016 (007) 004
53 (032) (024) (025) (035) 149 156 161 156 156 170 148 179 148 147 170 141 170 188 173 191
54 186 179 168 155 124 117 143 121 053 066 061 058 106 101 095 112 093 084 097 095
55 (135) (144) (137) (150) (190) (192) (201) (188) (170) (167) (161) (174) (185) (199) (201) (171) (296) (281) (273) (299)
56 (052) (051) (058) (040) (075) (083) (084) (087) (111) (124) (115) (109) (019) (009) 002 (019) (046) (047) (058) (039)
57 173 174 186 206 185 163 174 165 179 199 202 204 095 113 095 113 240 264 251 239
58 076 087 072 084 039 077 056 072 (005) 026 004 024 008 (004) (018) (002) (056) (058) (067) (061)
59 (072) (037) (056) (072) 059 073 063 057 048 051 060 051 074 102 100 080 033 037 022 034
60 (109) (093) (104) (106) (170) (175) (156) (156) (192) (185) (165) (178) (238) (234) (237) (243) (122) (127) (109) (150)
61 044 048 032 047 104 106 096 106 063 077 086 081 102 092 089 080 079 079 059 054
62 (105) (101) (086) (094) 009 (006) (009) (007) (027) (025) (023) (025) 026 015 030 041 (161) (157) (166) (184)
63 (095) (103) (105) (122) (077) (072) (095) (081) (123) (108) (120) (115) (077) (075) (081) (075) 020 018 028 018
64 (079) (070) (083) (086) 074 070 060 069 062 065 077 059 121 103 119 133 016 011 (005) 017
65 096 093 106 097 146 131 129 142 147 140 150 146 169 166 156 154 176 197 186 175
66 (049) (046) (037) (042) (084) (088) (081) (084) (110) (125) (112) (120) (149) (140) (151) (124) (042) (051) (064) (085)
67 061 045 052 064 096 126 115 098 107 100 105 103 157 142 150 155 009 034 031 029
68 (085) (114) (100) (117) (180) (194) (192) (178) (165) (186) (182) (184) (135) (123) (134) (140) (131) (134) (116) (157)
69 (084) (062) (057) (060) (071) (050) (052) (053) (104) (110) (109) (100) 028 002 031 008 (003) (012) (008) (004)
70 (002) 011 007 002 058 065 058 057 042 027 035 034 069 050 045 037 066 076 106 097
71 023 024 035 027 075 066 071 081 095 084 089 077 039 040 032 039 076 096 076 076
72 (005) (026) (008) (013) 061 048 062 036 027 026 034 017 121 143 116 129 (056) (025) (024) (015)
73 048 050 056 053 118 111 110 109 097 090 099 083 203 196 178 200 072 102 084 074
74 (119) (122) (110) (137) (040) (041) (045) (036) (033) (031) (018) (030) (071) (076) (086) (093) (044) (057) (050) (028)
75 114 095 103 110 098 087 111 092 026 041 026 023 109 106 108 120 027 029 017 023
76 057 048 022 022 085 080 076 090 086 072 090 077 167 169 163 169 098 084 086 103
77 190 171 152 155 112 132 124 105 111 085 105 108 040 053 065 059 046 074 069 075
78 077 059 048 077 080 079 090 080 111 120 103 119 089 077 070 073 059 064 042 045
79 118 100 116 107 005 (002) 006 011 010 031 016 013 (033) (034) (056) (059) 046 050 062 058
80 033 017 045 032 047 053 045 050 (010) 004 (012) (001) 066 093 082 077 045 034 044 048
81 182 197 190 205 085 101 107 099 114 122 103 125 081 073 071 071 197 192 209 220
82 076 081 062 078 (080) (086) (091) (084) (093) (085) (087) (086) (006) (014) (000) 009 082 069 074 061
83 156 153 147 166 006 (004) 020 003 (019) (018) (026) (025) (052) (052) (037) (048) (178) (171) (159) (160)
84 017 (010) (000) 003 048 058 066 040 042 051 037 048 020 052 037 030 006 006 (001) (000)
85 006 007 006 (000) 009 (020) (017) (013) 010 005 007 023 (045) (064) (063) (069) (131) (148) (138) (135)
86 078 080 076 077 162 150 131 145 144 154 144 151 281 273 262 269 091 085 107 097
87 (034) (019) (010) (011) (080) (060) (085) (074) (086) (087) (093) (097) (024) (034) (033) (036) (001) 001 010 017
88 (070) (046) (064) (058) (028) (040) (049) (046) (009) 005 (021) (021) 038 056 036 033 (045) (052) (050) (073)
89 (031) (037) (025) (013) (099) (097) (094) (098) (097) (107) (096) (084) (158) (167) (144) (163) (221) (230) (232) (238)
90 123 130 136 128 057 060 088 099 103 109 105 089 112 116 115 112 133 118 133 122
91 (146) (126) (133) (132) (037) (026) (029) (035) (010) (020) (038) (029) 000 (024) (020) (022) 059 068 077 069
92 (001) 007 (009) 018 (073) (053) (058) (058) (065) (078) (068) (070) (048) (052) (051) (052) (112) (143) (127) (123)
93 (226) (202) (225) (216) (056) (079) (073) (055) (020) (046) (045) (052) (011) (017) (014) (016) 027 021 039 037
94 (131) (113) (112) (115) (025) (019) (023) (024) (021) (013) (037) (047) (034) (005) (048) (023) (037) (042) (041) (042)
95 (087) (084) (091) (090) 066 068 041 072 116 108 119 121 152 144 136 136 152 180 159 168
96 (042) (035) (040) (038) (018) (001) (010) (018) 011 004 025 008 031 040 035 039 (011) (013) (012) (022)
97 (151) (157) (167) (162) (122) (147) (143) (134) (141) (132) (114) (134) (093) (074) (093) (093) (108) (119) (109) (132)
98 096 101 091 109 097 102 101 107 070 074 071 040 097 098 089 099 (062) (089) (089) (077)
99 033 028 029 021 (008) (017) (022) (009) (001) (010) 018 (002) 027 008 007 033 (038) (031) (030) (006)
100 (055) (051) (066) (067) 011 011 029 005 018 022 034 038 091 085 091 088 073 071 087 075
Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Simulated
5 011 009 007 006 407 327 289 267 3441 2649 2197 1553 165 147 131 120 1348 1197 1063 958
95 035 041 046 053 792 849 927 1028 10040 12591 13844 14919 295 320 360 373 2540 2843 3160 3469
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024
2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409
3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213
4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321
5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205
6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513
7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729
8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146
9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173
10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158
11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751
12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839
13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336
14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447
15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927
16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233
17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544
18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643
19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517
20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550
21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430
22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160
23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103
24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467
25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982
26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805
27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291
28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686
29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518
30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375
31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368
32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209
33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808
34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239
35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858
36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238
37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959
38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511
39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335
40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883
41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558
42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789
43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938
44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783
45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691
46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928
47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739
48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657
49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762
50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956
51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310
52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806
53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292
54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417
55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685
56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573
57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836
58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466
59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987
60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103
61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121
62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991
63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890
64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881
65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126
66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358
67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960
68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081
69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759
70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430
71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274
72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702
73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263
74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632
75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922
76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479
77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264
78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058
79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150
80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079
81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603
82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171
83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068
84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782
85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158
86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437
87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886
88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414
89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834
90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639
91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228
92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204
93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008
94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559
95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056
96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662
97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168
98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393
99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748
100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1
1
185947613236 209700016612 381289955056 113496909508 105940968217
261410846662 278202957602 636440926272 159777507841 164064511715
340758878594 374684278258 761658671012 238893166595 230844682326
456070302878 505106365162 869191059145 27405034829 302394276185
1 1 1
2 2 2
3 3 3
4 4 4
2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure
Sugar Sugar Sugar Sugar Sugar Sugar Sugar
Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas
Electricity Electricity Electricity Electricity Electricity Electricity Electricity
Diesel Diesel Diesel Diesel Diesel Diesel Diesel
Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum
1
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
USER INPUTS RISK PROFILE OUTPUTS
Date 1112 5 Holistic commodity profile and impact on earnings (baseline 95 confidence band 5 confidence band)
Price and Volatility Base 5th -ile 95th -ile
Q1 Q2 Q3 Q4 2012 Un hedged Hedged Impact Un hedged Hedged Impact
Price Volatility Price Volatlity Price Volatility Price Volatility of hedge of hedge
Sugar ($lbs) $ 020 70 $ 021 70 $ 022 70 $ 023 70 Sales 2069 2069 2069 - 0 2069 2069 - 0
Natural gas ($MMBTU) $ 573 48 $ 573 48 $ 573 48 $ 573 48 Cost of Goods Sold
Electricity ($MWh) $ 6227 69 $ 6227 69 $ 6227 69 $ 6227 69 Fixed Costs 480 480 480 - 0 480 480 - 0
Diesel ($gal) $ 224 37 $ 224 37 $ 224 37 $ 224 37 Sugar 119 58 58 - 0 311 311 - 0
Aluminum ($KTon) $ 1878 32 $ 1878 32 $ 1878 32 $ 1878 32 Natural gas 237 112 112 - 0 351 351 - 0
Electricity 249 87 87 - 0 381 381 - 0
Exposure risk level 95 Diesel 155 101 101 - 0 166 166 - 0
Aluminum 131 91 91 - 0 248 248 - 0
Commodity volumes (in millions) Variable Costs 892 450 450 - 0 1456 1456 - 0
Q1 Q2 Q3 Q4 Total COGS 1372 930 930 - 0 1936 1936 - 0
Sugar (lbs) 1280 1340 1410 1500 Gen amp Admin Exp 360 360 360 - 0 360 360 - 0
Natural gas (MMBTU) 96 101 106 111 EBITDA 337 779 779 - 0 (228) (228) - 0
Electricity (MWh) 10 10 10 10
Diesel (gal) 160 168 176 185
Aluminum (KTon) 16 17 18 19 4 Net commodity exposure 2012 by commodity (in millions $)
Other financial statement assumptions
Q1 Q2 Q3 Q4
Sales volume (in millions) 3200 3360 3528 3704
Sales price ($) 015 015 015 015
Fixed costs ($ millions) 120 120 120 120
GampA Exp ($ millions) 90 90 90 90
Hedge Selection
Futures
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
3 Gross commodity exposure 2012 (in millions $)
a) By quarter b) By commodity
All exposures reported as the difference between the 95th percentile highest exposure and the expected baseline gross commodity exposure
2 Estimated commodity volumes for 2012 by quarters (variable units in millions $)
1 Commodity price projections (baseline 95 confidence band 5 confidence band)
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
Read Me - File Overview
Page 4: Commodity Risk Management - Oliver Wyman

Historically most businesses could simply withstand changes in commodity prices given that the swings were usually temporary cyclicalmdashand manageable However structural changes in the global economy are creating wilder swings in commodity prices that are not only affecting short-term profits but also long-term planning and investment In this environment every company must develop a formal risk management approach to counter the growing volatility in commodity prices For corporate leaders this means building the infrastructure governance programs and analytical capabilities that can help them better manage their exposure to commodities

1 COMMODITY PRICES AND VOLATILITY

After a brief respite in 2009 the volatility that has come to define many commodity markets

roared back in the latter half of 2010mdashmuch to the dismay of businesses for whom oil

industrial metals and other raw materials comprise a significant share of their costs Crude

oil cracked the psychological barrier of $100 per barrel major food price indexes reached

record highs in the first quarter of 2011 and base and industrial metals such as copper and

aluminum also reached new highs mdashcreating significant pain for buyers (see Exhibit 1) Even

where prices pulled back the cost of many commodities remained higher than beforemdashthe

result of structural shifts in supply and demand on both a local and global scale (For a closer

look at the causes of volatility see Exhibit 2)

These commodity shocks are not only cutting into corporate profits but are testing the

abilities of even the best businesses to plan for and invest in the future Already in 2011 the

CEOs of consumer-facing companies as diverse as PepsiCo Kraft Kimberly-Clark and Levirsquos

have said they expect commodity price inflation to pose a significant challenge to continued

earnings growth over the next several years Those in the middle of the value chain have a

tougher challenge as they cannot easily raise prices to recover lost margins Few expect this

storm to pass quickly The 2011 World Economic Forum Global Risks Survey found corporate

executives in agreement that a key risk they face in the coming decade is extreme volatility in

energy and other commodity prices1

1 World Economic Forum ldquoGlobal Risks 2011rdquo 6th edition January 2011 Oliver Wyman was a contributor to that report

EXHIBIT 1 COMMODITY PRICES ARE SOARING INDEX

8

12

10

6

4

2

0

14

MULTIPLE OF INITIAL PRICEREAL PRICE HISTORY

1987 1991 1995 2003 20111999 2007

Crude Oil

Copper

Wheat

source Datastream 2011

Copyright copy 2011 Oliver Wyman 3

ExhIBIT 2 WhY ThE VOLATILITY

CAUSE DESCRIPTION

Erratic weather Catastrophic weather events have affected production (2009 drought in Australia affected global wheat prices)

Emerging markets Growth in emerging markets has increased demand for food energy and raw materials Global food output will have to rise 70 by 2050 to meet demand while global energy demand is predicted to increase by 36 between 2008-2035

Speculation Emergence of commodities as an investment class (development of commodities-based ETFs)

Infrastructure spending Deteriorating infrastructure in developed countries and a lack of infrastructure in emerging economies hampers the physical flow of commodities

Supply-country strategies Development of market-distorting trade policies (Russian wheat export ban followed crop shortfall in 2010)

Purchasing-country strategies

Countries developing more sophisticated buying capabilities (Korea opened a grain-trading office in Chicago in 2011)

Food and Agriculture Organization of the United Nations World Summit on Food Security November 2009

International Energy Agency World Energy Outlook 2010

This volatility could persist for years particularly given that governments appear willing to disrupt or intervene in markets including halting exports These collective forces have created pressure for corporations to develop strategies to mitigate this growing risk While companies in agri-processing oil refining and wholesale electricity generation have developed formal approaches to trading and risk management programs the majority of companies need to do moremdashmuch more

Given the likelihood of further volatility in commodity prices every company must adopt an analytic risk framework based on a clear understanding of its exposure This paper offers guidance on developing a plan for managing commodity risks that draws on the best practices of companies from around the world The approach is built around the three pillars of a ldquobest practicesrdquo program governance infrastructure and analytics The paper provides an in-depth review of the role of analytics and outlines a new systematic approach to managing commodity risk illustrated through case studies

ONE COMPANYrsquoS MOVES TO TAME ITS VULNERABILITY TO VOLATILITY

A leading industrial company failed to deliver its projected earnings due to a decline in the profitability of non-core energy activities To measure the companyrsquos total exposure to energy volatilitymdashand quantify the potential effect on future profits it needed to develop an integrated energy risk profile In other words the company needed the ability to aggregate the energy exposure of each business unit and evaluate the effectiveness of existing mitigation efforts before it could truly understand net exposure

The company analyzed commodity volatilities and correlations to produce a probabilistic analysis and derive ldquoEPS-at-riskrdquo estimates demonstrating the portion of earnings that were vulnerable to these price volatilities It then adopted a risk assessment tool capable of performing scenario analysis linked to alternative market states and specific events integrating this discipline by training both operational and financial staff in Treasury Procurement Planning and Operations This helped the company to gain insights into the aspects of its energy exposure that were most sensitive to price movements under different future states and thus were targets for mitigation efforts

Copyright copy 2011 Oliver Wyman 4

2 COMMODITY RISK MANAGEMENT FRAMEWORK

DEVELOPING A COMMODITY RISK GOVERNANCE PROGRAM

A crude oil producermarketer in Eastern Europe was recently seeking to enhance margins by creating regional physical trading capability First the Board required stakeholder alignment on a multi-dimensional risk appetite statement to guide risk and scale considerations High level loss limits were linked to the risk appetite and from these ldquodesk-levelrdquo limits were calculated By codifying the risk limits in policies the equity usage growth targets and working capital requirements were made explicit and served to support all later decisions on the business expansion plan

A structured commodity risk management framework is built around three pillars

Governance Infrastructure and Analytics Together these pillars support both short-

term tactical decisions and long-term strategic initiatives (see Exhibit 3)

GOVERNANCE

The first pillar of this framework is Governance in which an organization identifies the main

objectives of its commodity risk management program Companies first create a risk

appetite statement In this critical step an organization defines its risk tolerance and

aligns it with its broader performance objectives The risk appetite statement thus

establishes the basis of the organizationrsquos risk limits

ExhIBIT 3 COMMODITY RISK MANAGEMENT FRAMEWORK

SHORT-TERM TACTICAL DECISION MAKING

LONG-TERM STRATEGIC DECISION MAKING

GOVERNANCE

bull Risk appetite and corporate objectivesbull Risk limits bull Risk policybull Delegations of Authoritybull Decision frameworks

INFRASTRUCTURE

bull Reportingbull Accountingbull ITsystemsbull Organizational designbull Human capital

ANALYTICS

bull Price forecastingbull Commodity exposure modeling bull Stress and scenario testingbull Hedge optimization

Copyright copy 2011 Oliver Wyman 5

INFRASTRUCTURE

The second pillar Infrastructure comprises the organizational structure systems and

human capital that companies need to measure and manage risks Organizations need to

assess whether they have the systems to capture and monitor the necessary data flows The

organizational structure is important because the volatility in commodity markets requires

discipline and a tight alignment between managing cost- and revenue-related risks across

the enterprise historically these functions worked independently but today that results in

inefficiencies or even conflicting actions by different internal teams human capital is also

key In some cases a corporation may not have the experience or capabilities to manage

commodity risks effectively While a company can outsource risk management to financial

institutions or other market participants it pays a significant premium for that service and

potentially forfeits any upside potential In the process it gives its ldquopartnerrdquo proprietary

information they can use to trade for solely their own benefit

INCREASING EFFECTIVENESS ThROUGh IMPROVED INFRASTRUCTURE

The effects of steady growth were slowly compromising the risk management effectiveness of a major North American crude and distillates marketertrader Reporting timeliness was eroding error rates were climbing and managementrsquos confidence in risk control was decreasing prompting a full review of the organizational design and processes This revealed that the risk control function (middle office) was still performing much of the trade reconciliation that trade details were transmitted by email and that most reporting was done through spreadsheets

Several straightforward organizational shifts and systems enhancements eliminated a number of bottlenecks and enabled risk reporting down to the cargo level by desk and counterparty

ANALYTICS

The third pillar of a commodity risk management program is Analytics Organizations

cannot assess their financial exposure to commodity price swings without robust analytical

tools These analytics (or ldquomodelingrdquo) platforms support decision making at every level

mdash by helping managers model the future paths of commodity prices conduct stress- and

scenario-testing and evaluate and optimize risk-return profiles using a range of price-risk

management strategies however analytical capabilities vary dramatically from firm to firm

Companies should take a sequential approach to developing a robust analytics framework

that will support commodity risk management

Copyright copy 2011 Oliver Wyman 6

ThINK LIKE A TRADER

Trading is viewed as a necessary evil by some corporations given the connotations of excessive speculation and risk-taking with derivatives (in this case commodity futures contracts) A recent review of annual reports reveals that many companies disclosing commodity positions caution that these are strictly for hedging purposes and that the company ldquodoes not hold financial derivative positions for the purposes of tradingrdquo

Whether they realize it or not many companies are in fact speculating on commodity pricesmdashon both the cost and revenue sides Procurement officers are tasked with getting the best price on purchases for the company and its business units So the procurement staff commonly enters into forward fixed-price arrangements to guarantee both supply and pricing Just like traders they have thus made a bet on future prices and concluded price risk management with that view in mind

Companies need to accept that the days of a ldquoset it and forget itrdquo approach to risk management are over That approach was fine when volatility was low and prices increased gradually over time Today companies need to empower designated executives to think like traders ldquoIs this a good or bad price Should I buy more or run down inventoryrdquo Since most companies arenrsquot traders by nature they need to create a trading playbook that is integrated with their overall game plans

First a company must understand its commodity risk profile This helps the organization

assess its net exposure to commodity pricesmdashand their inevitable volatilitymdashacross

business and customer segments Detailed analysis also allows the organization to then

develop long-term strategies to mitigate commodity-related risks

A commodity risk profile provides a common understanding for senior management and

a fact-based foundation for evaluating the effectiveness of risk-mitigation actions With

this knowledge management can determine if the companyrsquos current commodity risk

exposure is within its risk tolerance and whether it has communicated these expectations

to stakeholders This analysis also helps the management team evaluate different risk

management strategies It promotes risk mitigation at the portfolio level which helps

reveal any risks lurking in individual business units or departments In short this analysis

will allow the company to optimize risk-return positioning

ThE NET ExPOSURE ChALLENGE

Determining true net exposure requires consideration of several factors gross commodity exposure existing risk mitigations foreign exchange flows demand sensitivity and the interactions among these This can be challenging for an enterprise with wide-spread operations or multiple product lines Typically each factorrsquos net impact at the corporate level is first assessed and any offsets across factors can then be accounted for

Copyright copy 2011 Oliver Wyman 7

To manage commodity price risks in ways that are consistent with broader corporate

objectives companies need a robust set of analytic tools to calculate the current exposure

Exhibit 4 offers a six-step analytical approach that companies can use to determine the

effect of commodity price risks and mitigation efforts on key financial metrics

The first step in the analytical framework requires management to build a forecast

of commodity prices using simulations or other techniques The company should

complement these forecasts with an analysis of alternative price outcomes based on ldquostress

eventsrdquo to understand fully how prices may evolve

In the second step the management team estimates the volume of future commodity

purchases across the enterprise In Step 3 this demand forecast is combined with

price projections to determine the firmrsquos gross commodity exposure In Step 4 the risk

management strategies already in place are identified Then in Step 5 these are applied

against the gross exposure to generate a ldquonet commodity exposurerdquo for the enterprise The

process ends with Step 6 the creation of a holistic company-wide risk profile ndash with the

sensitivities in price projections identified in Step 1 used to explore the potential impact on

EBITDA debt covenants and other financial metrics

Copyright copy 2011 Oliver Wyman 8

ExhIBIT 4 CREATING A hOLISTIC COMMODITY RISK PROFILE

bull Use analytic engines to simulate potential commodity price pathways (data driven)

bull Incorporate market context and paradigm shiftsscenario analysis ( judgment driven)

bull Integrates historical patterns market intelligence and fundamental analysis

bull Unifies disparate views of expected and highlow commodity price scenarios across organization

bull Incorporates interrelationships and correlations between commodities and currencies

STEP ANALYSIS BENEFITS

bull Centralizes commodity requirements across business units and geographies

bull Enables testing of alternative commodity purchase requirements using price elasticity analysis

bull Determine expected commodity purchase volume based on sales expectations

bull Gives context of expected commodity exposure compared to PampL and other risks

bull Accounts for ldquonatural hedgesrdquo in the portfolio

bull Shows shifts due to changes in business mix commodity price expectations

bull Calculate expected commodity exposure (eg price multiplied by volume)

bull Brings together and coordinates dierent functions of the organization (eg Strategy Procurement Treasury) and geographies

bull Sets understanding of risk management options for key commodity exposures

bull Define options for managing commodity price risk

bull Centralize risk management options undertaken across organization

bull Helps management assess the eectiveness of the risk management portfolio vs desired exposure

bull Provides understanding of how commodity price risk flows through the organization

bull Calculate exposure after incorporating current risk management portfolio

bull Enables objective evaluation and comparison of a range of risk management strategies

bull Promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at individual business unit or department level

bull Provides view of the earnings impact from commodity prices at dierent levels of probability

bull Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

COMMODITY PRICEPROJECTIONS

SALES PRICINGANDPURCHASE VOLUMES

GROSS EXPOSURE

RISK MANAGEMENTPORTFOLIO

NET EXPOSURE

HOLISTIC COMMODITYRISK PROFILE

Source Oliver Wyman

Copyright copy 2011 Oliver Wyman 9

IMPROVING COMPETIVENESS WITh A STRATEGIC RESPONSE TO COMMODITY VOLATILITY

A global food ingredient processor was losing sales to much smaller competitors solely because those firms were much more responsive to the highly volatile product price By analyzing the impacts of its forward sales process and market price uncertainty a new strategy was developed to migrate customers to shorter term contracts and to begin linking prices to market indices ndash which immediately improved the firmrsquos competitive positioning

STRATEGIES FOR MANAGING COMMODITY RISK

With a well-defined risk profile and an understanding of its ability to manage risk a

company can build a plan that matches its net exposure to commodities with its tolerance

for risk To manage commodity prices in the short term companies generally have three

tools at their disposal

bull Product pricingmdash identifying customer segments where the company has the ability to

raise prices or create pricing structures that mitigate risk

bull Procurement contract structuringmdashdeveloping innovative risk-sharing contracts with

suppliers

bull Financial hedgingmdashusing financial instruments for hedging to reduce overall

risk exposure

The effectiveness of these short-term strategies depends on the size of the organizationrsquos

exposure to a given commodity mdash and the commodity itself For example financial hedging

works best in commodity markets that are liquid (eg energy products such as crude oil and

natural gas or agricultural products such as wheat and corn) Meanwhile passing higher

commodity costs to customers through price increases is often ineffective in competitive

markets such as consumer products however when commodity cost increases are

significant and widespread pass-through pricing might be more viable Some companies

have taken creative approaches to raising prices For instance a number of consumer

products companies have reduced the volume of productmdashwhile keeping the same

package sizemdashto maintain margins in the face of higher commodity costs Other firms have

substituted cheaper ingredients to lower their net product costs

Over the long term volatility in commodity prices affects the behaviors of consumers

companies and their suppliers In response some companiesmdashand even countries--have

embedded commodity risk strategies into their long-term strategic plans

Copyright copy 2011 Oliver Wyman 10

CONCLUSION

Large and sustained commodity price swings are reshaping whole industries This means

that commodity risk management can no longer be considered the sole responsibility of

the procurement or finance staff With rising commodity prices affecting both the short-

term earnings and long-term strategies it is imperative that C-level executives develop a

deeper understanding of how to mitigate these risks Developing a structured commodity

risk management program built around the components outlined here is crucial Given

the new realities of higher prices and more volatility in commodities organizations must

integrate commodity risk management into their day-to-day operations They also must

build it into their long-term strategies to ensure the viability of the firm itself

ABOUT THE AUTHORS

MICHAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman

Michael J Denton PhD is a New York based Partner in Oliver Wymanrsquos Global Risk amp Trading practice with specialized experience in

energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market

risk modeling portfolio dynamics and risk-based decision frameworks Recent projects have focused on due diligence evaluations

competitive contracting and counterparty credit risk mitigation

ALEX WITTENBERG

Partner Global Risk amp Trading Oliver Wyman

Alex is the Managing Partner of Oliver Wymanrsquos Global Risk Center and has over 20 years of cross-industry experience in risk management

advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision-making and financial performance designing

risk governance for Boards and Management and developing corporate risk monitoring mitigation and transfer frameworks

Copyright copy 2011 Oliver Wyman 11

Copyright copy 2011 Oliver Wyman All rights reserved This report may not be reproduced or redistributed in whole or in part without the written permission of Oliver Wyman and Oliver Wyman accepts no liability whatsoever for the actions of third parties in this respect

The information and opinions in this report were prepared by Oliver Wyman

This report is not a substitute for tailored professional advice on how a specific financial institution should execute its strategy This report is not investment advice and should not be relied on for such advice or as a substitute for consultation with professional accountants tax legal or financial advisers Oliver Wyman has made every effort to use reliable up-to-date and comprehensive information and analysis but all information is provided without warranty of any kind express or implied Oliver Wyman disclaims any responsibility to update the information or conclusions in this report Oliver Wyman accepts no liability for any loss arising from any action taken or refrained from as a result of information contained in this report or any reports or sources of information referred to herein or for any consequential special or similar damages even if advised of the possibility of such damages

This report may not be sold without the written consent of Oliver Wyman

wwwoliverwymancom

Oliver Wyman is a leading global management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development Oliver Wymanrsquos Global Risk Center is dedicated to analyzing increasingly complex risks that are reshaping industries governments and societies Its mission is to assist decision makers in addressing risks by combining Oliver Wymanrsquos rigorous analytical approach to risk management with leading thinking from professional associations non-governmental organizations and academic institutions For further information please visit wwwoliverwymancomglobalriskcenter

The Association for Financial Professionals (AFP) headquartered outside Washington DC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFP is the daily resource for the finance profession

For more information please contact

MIChAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman +16463648423 michaeldentonoliverwymancom

ALEx WITTENBERG

Partner Global Risk amp Trading Oliver Wyman +16463648440 alexwittenbergoliverwymancom

BRIAN T KALISh

Director Finance Practice Lead +13019616564 bkalishafponlineorg

Oliver Wyman
File Attachment
Commodity volatilitypdf

intRoduCtion

given the current and future trends in commodity prices and volatility every company must

better understand its true commodity exposure Companies can often be squeezed by

rising and volatile commodity input prices that cannot be passed along to customers in

their entirety A commodity risk management program can help not all organizations can

or should adopt the sophisticated mechanisms of a pure commodity business however

most organizations particularly those in the middle of the value chain can improve their

commodity risk analytics

exhibit 1 CompAnies ARe ChAllenged by Rising Commodity volAtility

Output prices

bull Competitive pressuresbull Limited ability to pass

through higher costs

COMPANY

bull Understand current forecasts (starting position and price curve)

bull Trace impact of price volatility to earnings

bull Align commodity risk management program strategy with corporate objectives

Commodity risk management program strategy instruments etc

Input prices

bull Increasing volatilitybull Increasing absolute pricebull Breakdown in correlations

the starting point is to understand the companyrsquos holistic commodity risk profile using

analytics and modeling tools A holistic commodity risk profile helps the organization assess

its individual and net exposure to commodity pricesmdashand the inevitable volatilitymdashacross

business and customer segments on a forward-looking basis the profile provides a common

understanding for senior management and a ldquofact-basedrdquo foundation for evaluating the

effectiveness of current risk-mitigation actions and alternative risk management strategies

With this knowledge management teams can determine if current commodity risk exposure

is within the companyrsquos risk tolerance and communicate these expectations to stakeholders

it also helps to promote risk mitigation at the portfolio level by identifying the most

important drivers of overall risk and ensuring the capture of any offsetting risks that may be

present in short this analysis will allow the company to optimize risk-return positioning

this In Practice Guide provides an overview of a stepwise approach and analytic processes

necessary to develop an organizationrsquos net commodity exposure

Copyright copy 2012 oliver Wyman 3

six steps to deteRmining Commodity exposuRe And impACts

exhibit 2 provides an overview of a six-step analytical approach to determine the impact of

commodity price risks on key financial metrics

exhibit 2 six steps to CReAte A holistiC Commodity Risk pRoFile

6 HOLISTIC COMMODITY RISK PROFILE

5 NET EXPOSURE

4 RISK MANAGEMENT

PORTFOLIO

3 GROSS EXPOSURE

2 SALES PRICING AND COMMODITY

PURCHASE VOLUMES

1 COMMODITY PRICE

PROJECTIONS

STEPS

ANALYSISUse analytic engines to simulate potential commodity price pathways (data driven)

Incorporate market context and paradigm shifts scenario analysis (judgement driven)

Calculate expected commodity exposure (ie price multiplied by volume)

Define options for managing commodity price risk

Centralize risk management options undertaken across organization

Calculate exposure after incorporating current risk management portfolio

Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

Determine expected commodity purchase volume based on sales expectations

Copyright copy 2012 oliver Wyman 4

step 1 Commodity pRiCe pRojeCtions

CoRe AnAlysis user-defined baseline commodity prices and volatilities for a predefined set of commodity

inputs are incorporated into a standard simulation process to generate a distribution of

potential future price paths for each commodity

outputs A distribution of terminal price values for each commodity and time horizon

beneFits bull integrates historical patterns market intelligence and fundamental analysis

bull unifies views of commodity price scenarios across the organization

bull incorporates interrelationships and correlations between commodities and other price

risk factors (eg currencies)

the first step in the analytical framework requires

management to build assumptions for commodity

prices in the future using simulation or other techniques

the management team should complement these

forecasts with an analysis of alternative price outcomes

based on ldquostress eventsrdquo to understand fully how prices

may evolve

A simple price simulation model is presented in the

attached workbook (accessed through the pushpin

icon) the inputs for these simulations are located on

the tab labeled ldquointerfacerdquo under the heading ldquoprice

and volatilityrdquo the user defines base case prices and

volatilities for each commodity and forward period

(ie 1st quarter 2nd quarter and so on) the simulated

terminal value prices or outcomes for each commodity

and time horizon are shown on the tab labeled ldquoCmdy

price projrdquo each of these values reflects possible future

outcomes for commodity prices based on three factors

mean price volatility and time

the outputs of the simulations for each commodity are

summarized on the ldquointerfacerdquo page in the ldquoCommodity

price projectionsrdquo section in each case 5th and 95th

percentile outcomes are shown along with the baseline

price scenario over the course of the time period to

provide a richer understanding of possible outcomes

versus a single static forecast not surprisingly the

uncertainty of price forecasts grows with the increasing

time horizon

Copyright copy 2012 oliver Wyman 5

ReadMe

OverviewThe workbook has been prepared as a supplement to the In Practice Guide Six Steps to Assess Commodity Risk Exposure prepared by Oliver Wyman for AFP members and available at wwwafponlineorg The purpose of this workbook is to provide a simple tool for illustration purposes only to better understand and quantify commodity risk impacts on earmings The example focuses on a corporation which utilizes key commodities including natural gas diesel fuel sugar electricity and aluminum The components of the workbook include a primary Interface worksheet as well as support worksheets which house the calculations used to generate outputs on the Interface sheet Note that all user inputs and outputs are exclusively contained in the Interface sheet - all other sheets are for reference only Cells in white in the User Inputs block represent input cells (prices volatilities and hedge designation) Any changes in outputs are generated by using the Calculate button The Interface worksheet receives inputs and provides outputs for a probabilistic earnings forecast model and illustrates the impacts of commodity prices and volatilities on the earnings profile In particular each of the outputs reflects one of the steps in the 6 step process Outputs include(1) Commodity price projections - User-defined baseline commodity prices and volatilities for a pre-defined set of commodity inputs are incorporated into a standard simulation process to generate a distribution of potential future price paths for each commodity(2) Estimated commodity volumes - A predefined set of commodity volumes reflecting commodity demand over time(3) Gross commodity exposure - User-defined baseline prices are combined with simulated price paths and volumes to generate commodity exposure - measured as the difference between the 95th percentile worst case outcome and the baseline expected outcome (4) Net commodity exposure - Gross commodity exposure is adjusted for any financial or physical hedges for each of the commodities Hedges are assumed to lock in the commodity at the baseline expected price Price correlations between commodities are taken into account(5) Holistic commodity risk profile - Commodity exposures are combined with predefined estimates for revenues and other fixed costsSGA to illustrate both positive and negative impacts on the earnings profileKey assumptionsKey assumptions in the model include the following - A GBM process is used to simulate all of the commodity price paths - Correlations are predefined and fixed resulting in a static set of shocks used in the simulation process- User defined volumes and revenues are static- User defined prices and volatiliities are used as baseline assumptions for each quarter

Interface

Interface

REF
1

Cmdy Price Proj

Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
124418764137
1367693617635
2648580611483
786217932233
803244438443
5605736599794
124418764137
4238042982159
1367693617635
1589462370676
2648580611483
803244438443
786217932233
0
803244438443

Correlations

5
95
Mean
Sugar
01107087317
03452715728
02
00854552129
04050827214
021
00695646039
04616729635
022
0061034605
05340468686
023

Sales Pricing Volumes

Sugar
Natural gas
Electricity
Diesel
Aluminum

Gross Exposure

REF
EPS-at-risk
1

Risk Mgmt Portfolio

REF
EBITDA
1

Simulation Net Exposure

Diesel
Electricity
Natural gas
Crude oil
Price change
EPS impact
Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
1912572265773
1138117311849
1319658025171
11406289411
1162397994146
3734236225276
1912572265773
2596118913427
1138117311849
1276460888256
1319658025171
1162397994146
11406289411
0
1162397994146
5
95
Mean
Natural Gas
40688610903
79154680028
57310928297
32742030186
84855775584
57310928297
28887025303
92658501718
57310928297
26679947564
10281600624
57310928297

Net Exposure

5
95
Mean
Electricity
344141498094
1003995388957
622705433902
264877650618
1259146360173
622705433902
219723962528
1384358671012
6227
15528763279
1491896493046
622705433902

Fin Statement

5
95
Mean
Diesel
16508189205
29538139317
22444582473
14672697038
31955148416
22444582473
13074767002
36018057848
22444582473
11953891033
37258114813
22444582473

Price shock contuity

5
95
Mean
Aluminum
134769028369
254013105136
1878
119721652401
284279599425
187771063122
106347176349
316018108859
187771063122
95839609124
346925945325
187771063122

Graph support

Sugar
Natural gas
Electricity
Diesel
Aluminum
128
96
1
16
16
134
101
1
168
17
141
106
1
176
18
150
111
1
185
19
95ile
5ile
Earnings statement
-161353539101
1326569803377
611516334878
-421367106558
1798147036403
760774397443
-642177203637
2144399264928
918591594906
-1050874848209
2521161989213
1080153466317
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Correlations among commodity prices dictate the way in which commodity prices move together and are used to develop commodity price projections via simulation
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Commodity volumes may be determined by reviewing sales and demand planning forecasts Volumes may be aggregated when sourced from different business divisions or regions to arrive at a corporate-wide volume estimate
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Oliver Wyman
File Attachment
AFP-OW Commodity Risk Calculatorxls

step 2 sAles pRiCing And puRChAsing volumes

CoRe AnAlysis user-defined commodity volumes for the predefined set of commodities are coupled

with the distribution of price outcomes to support the determination of gross and net

exposures to commodities sales volumes and prices are incorporated into the pro forma

financial statement to understand earnings impacts from commodity exposures

outputs A predefined set of commodity volumes reflecting commodity demand over time

beneFits bull Centralizes commodity requirements across business units and geographies

bull enables testing of alternative commodity purchase requirements using price

elasticity analysis

in the second step the management team must

estimate the volume of future commodity purchases

across the enterprise the estimated volumes for inputs

are going to be directly linked to sales forecasts and

thus may be derived from product demand profiles

While the current model focuses on price uncertainty

it is important to recognize the presence of volume

uncertainty as well the static volumes are estimated

and then entered by the user on the tab labeled

ldquointerfacerdquo under the heading ldquoCommodity volumesrdquo

Assumptions for product sales volumes and prices are

also entered on the ldquointerfacerdquo tab for later use in the

holistic commodity risk profile assessment

step 3 gRoss exposuRe

CoRe AnAlysis user-defined baseline prices are combined with simulated price paths and volumes to

generate commodity exposure as measured by the difference between the 95th percentile

worst case outcome and the baseline expected outcome

outputs A firmrsquos gross commodity exposure given commodity price and volume

beneFits bull gives context of expected commodity exposure in comparison to pampl and other risks

bull shows changes over time due to either changes in business mix or changes in

commodity price expectations

in the third step the expected gross commodity

exposure is determined the 95th percentile worst case

commodity costs located on the tab labeled ldquogross

exposurerdquo are found by multiplying each commodity

volume by price gross exposure is determined by

summing the difference between the baseline cost and

the simulated 95th percentile worst case cost for each

individual commodity exposure Alternative percentiles

may be selected using the ldquoexposure risk levelrdquo input

value on the ldquointerfacerdquo tab

Copyright copy 2012 oliver Wyman 6

step 4 Risk mAnAgement poRtFolio

CoRe AnAlysis Risk management strategies currently in place are identified

outputs portfolio of existing risk management options (eg a set of commodity derivative hedges)

beneFits bull brings together and coordinates different functions of the organization (ie strategy

sales and marketing procurement treasury) and geographies

bull sets common understanding of risk management options for key commodity exposures

After determining gross commodity exposure the next

step in the process is to identify existing risk management

strategies the tab labeled ldquoRisk mgmt portfoliordquo has

a predefined list of risk management options available

which limit the management decision to financial

hedges that are assumed to lock-in the commodity at the

baseline expected price on the tab labeled ldquointerfacerdquo

under the heading ldquohedge selectionrdquo the user selects

ldquoyesrdquo or ldquonordquo for each commodity depending on whether

the firm plans to hedge that particular commodity it

is important to note that management of commodity

price risk is not limited only to financial hedging rather

it typically involves a combination of four broad levers

ndash each (or a combination thereof) may be effective as

illustrated in exhibit 3

besides recognizing current risk management

strategies this step also alerts management of other

potential risk mitigation options that may be available to

manage key commodities exposures

Copyright copy 2012 oliver Wyman 7

exhibit 3 Risk mAnAgement options

a Product Pricing (incl ldquoPass-throughrdquo)

b Procurement contracts

c Financial hedging

d Value chain integration long-term strategies

oVerView bull use market pricing

power to recover

change in input costs ndash

revenue management

bull balancesharetransfer

risk on purchases ndash

cost management

bull transfer risk to third

party through markets

structured deals

(Reinsurers also providing

capacity here)

bull up or downstream

integration eg

sourcing raw materials

retail trading

examPle strategies

bull employ cost pass-through

mechanisms (across

all or a portion of the

product portfolio)

bull Align price risk transfer

mechanisms with sales and

purchase contracts

bull include contract

mechanisms that mitigate

risk (eg price ceilings

floors price bands knock-

inknock-out clauses)

bull hedge price exposure

with swaps to set the price

or options or collars to

limit downside

bull Cross-hedge exposure

with contracts with

strong correlation to

underlying exposure

bull Acquire upstream company

to ensure supply and

diversify risk exposure

bull identify drivers that

contribute greatest

percentage to overall risk

Pros bull Raw materials costs fully

or partially passed through

to customers

bull stabilized margins and

earnings certainty

bull protection against price

moves and volatility

bull transition risk back

to supplier under

certain structures

bull protection against

feed stock price moves

and volatility

bull no impact on product

pricingsupplier contracts

bull holistic approach to cost

and margin management

bull diversification of

risk exposure

cons bull Customers may substitute

products or move to

lower priced competitor

brands in response to

product price

bull suppliers demand

premium (higher price)

for risk sharing

bull need to find

receptive suppliers

bull liquidity premium and

market non-availability

for many commodities

bull basis risk from

cross-hedging

bull Requires appropriate

capital and expertise

for joint ventures

or acquisitions

bull increased organizational

complexity

Copyright copy 2012 oliver Wyman 8

step 5 net exposuRe

CoRe AnAlysis predefined financial hedges and price correlations between commodities are applied to

gross commodity exposure to determine net commodity exposure for the enterprise

outputs A firmrsquos net commodity exposure after incorporating its current risk management portfolio

beneFits bull Accounts for natural hedges

bull provides management with an understanding of the effectiveness of the risk

management portfolio vs desired exposure

bull provides broad understanding of how commodity price risk flows through

the organization

While calculating net exposure management must also

take into account the correlation between commodity

prices price movements of one commodity may be related

to the price movement of other commodities thereby

increasing or decreasing exposure depending on the

relative strength of the relationship to account for this the

net exposure of the portfolio must be determined on the

tab labeled ldquosimulation net exposurerdquo the distribution

of terminal price values for each commodity and time

period (eg 1st quarter 2nd quarter etc) are multiplied

against volume for that same time period A distribution

of net exposures is generated and 5th and 95th percentile

outcomes for the portfolio series are selected

A companyrsquos net exposure to commodity price volatility is

shown on the tab labeled ldquonet exposurerdquo the predefined

volume levels are multiplied by the new correlated prices

to determine the 95th percentile worst case net exposure

if the company is employing a hedging strategy for a

commodity then it has no exposure for that particular

commodity since hedges are assumed to lock-in the

commodity at the baseline expected price

step 6 holistiC Commodity Risk pRoFile

CoRe AnAlysis Commodity exposures and price projections are combined with predefined estimates for

revenues and other fixed costssgA to illustrate both positive and negative impacts on the

earnings profile

outputs pro forma financial statement with predefined revenue and fixed costsgA reflecting the

impact of price variations and hedging on earnings

beneFits bull enables objective evaluation and comparison of a wide variety of risk

management strategies

bull promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at an

individual bu level

bull gives management a view of earnings impact from commodity prices at different levels

of probability

the process of determining net commodity exposure

ends with the creation of a holistic company-wide

risk profile ndash with the sensitivities in price projections

identified in step 1 used to explore the potential impact

on ebitdA debt covenants and other financial metrics

this impact is shown on the tab labeled lsquointerfacersquo in the

section called ldquoholistic commodity profile and impact

on earningsrdquo

Copyright copy 2012 oliver Wyman 9

ConClusion

large and sustained commodity price swings are quickly reshaping industries and the

businesses that exist within them in this environment commodity risk management must

be firmly integrated into the strategy of the company and structured under a commodity risk

management framework

executives need to recognize that their strategies may have to change to accommodate how

volatile commodity prices are redefining their competitive landscape and develop a process

to manage them across functions decisions around commodity risk management must be

based on a forward-looking view this process can include a series of tough questions about

the sustainability of business models such as

bull Will a perception of scarcity resulting from sustained volatile commodity prices impact

that commodityrsquos availability

bull Will changing commodity correlations invalidate diversification plays to hedge against

other commodities

bull Will consumers shift to cheaper private label alternatives or change their lifestyles to

avoid expenses introduced by higher commodity prices

the companies who are first to define their risk management metrics to include extreme

prices and anticipate how they can best benefit from them will have a significant

competitive advantage

Copyright copy 2012 oliver Wyman 10

Copyright copy 2012 oliver Wyman 11

For more information please contact

michael denton Partner global risk and trading oliver wyman

+16463648440 michaeldentonoliverwymancom

michael denton is a new york-based partner in oliver Wymanrsquos global Risk amp trading practice with specialized experience in energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market risk modelling portfolio dynamics and risk-based decision frameworks

alex wittenberg Partner global risk and trading oliver wyman

+16463648440 alexwittenbergoliverwymancom

Alex Wittenberg has over 20 years of cross-industry experience in risk management advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision making and financial performance designing risk governance for boards and management and developing corporate risk monitoring mitigation and transfer frameworks

brian t Kalish director Finance Practice lead

+13019616564 bkalishafponlineorg

brian t kalish is the director and Finance practice lead of the Association of Financial professionals he is responsible for the Finance practice which includes Corporate Finance Risk management Capital markets investor Relations Financial planning amp Analysis Accounting and Financial Reporting

oliveR WymAn And the AssoCiAtion FoR FinAnCiAl pRoFessionAls the FinAnCiAl insights seRies

this is part of series of papers by international management consulting firm oliver Wyman and the Association for Financial professionals that address critical issues for finance professionals oliver Wyman and AFp welcome your thoughts on the ideas presented in this paper if you would like to provide feedback or be alerted when we schedule events or other activities for corporate finance professionals please contact one of the individuals listed below

oliver Wyman is an international management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development

the Association for Financial professionals (AFp) headquartered outside Washington dC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFp is the daily resource for the finance profession

wwwoliverwymancom

Copyright copy oliver Wyman All rights reserved

AuthoRs

michael denton Partner global risk and trading oliver wyman

alex wittenberg Partner global risk and trading oliver wyman

brian t Kalish director Finance Practice lead

Gross Exposure graph support (stacked graph and waterfall)
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 5606 4238 1589 803 00
Grey 1244 1368 2649 786 803
Sugar 1244 186 261 341 456 1244
Natural gas 1368 210 278 375 505 1368
Electricity 2649 381 636 762 869 2649
Diesel 786 113 160 239 274 786
Aluminum 803 106 164 231 302 803
Commodities volume graph support
Q1 Q2 Q3 Q4
Sugar 12800 13400 14100 15000
Natural gas 960 1010 1060 1110
Electricity 100 100 100 100
Diesel 1600 1680 1760 1850
Aluminum 160 170 180 190
Net exposure graph support
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 3734 2596 1276 1162 00
Grey 1913 1138 1320 114 1162
Sugar 1913 207 404 530 771 1913
Natural gas 1138 168 250 348 372 1138
Electricity 1320 221 266 331 502 1320
Diesel 114 26 39 12 38 114
Aluminum 1162 151 223 340 448 1162
Financial statement graph support
Cost (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 463 686 840 1116 3105
Volume (lbs) 1280 1340 1410 1500 463 686 840 1116 3105 Unhedged
Price 036 051 060 074
Natural gas 718 829 956 1008 718 829 956 1008 3511
Volume 96 101 106 111 718 829 956 1008 3511 Unhedged
Price 75 82 90 91
Electricity 844 888 953 1125 844 888 953 1125 3810
Volume 10 10 10 10 844 888 953 1125 3810 Unhedged
Price 844 888 953 1125
Diesel 385 416 407 453 385 416 407 453 1660
Volume 160 168 176 185 385 416 407 453 1660 Unhedged
Price 24 25 23 24
Aluminum 451 543 678 805 451 543 678 805 2477
Volume 16 17 18 19 451 543 678 805 2476841735006 Unhedged
Price 282 319 377 424
Total 2861 3361 3834 4507 2861 3361 3834 4507 14564
2861 3361 3834 4507 14563772697504 Unhedged
Cost (5)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 179 142 141 119 179 142 141 119 581
Volume (lbs) 1280 1340 1410 1500 179 142 141 119 581 Unhedged
Price 014 011 010 008
Natural gas 347 283 251 241 347 283 251 241 1123
Volume 96 101 106 111 347 283 251 241 1123 Unhedged
Price 36 28 24 22
Electricity 332 223 176 138 332 223 176 138 868
Volume 10 10 10 10 332 223 176 138 868 Unhedged
Price 332 223 176 138
Diesel 275 264 244 231 275 264 244 231 1015
Volume 160 168 176 185 275 264 244 231 1015 Unhedged
Price 17 16 14 12
Aluminum 241 230 236 205 241 230 236 205 911
Volume 16 17 18 19 241 230 236 205 911127595797 Unhedged
Price 150 135 131 108
Total 1373 1142 1048 935 1373 1142 1048 935 4498
95ile 5ile
Sales 480 504 529 556 480 504 529 556
Fixed Costs 120 120 120 120 120 120 120 120
Variable Costs 286 336 383 451 137 114 105 93
General amp Admin Exp 90 90 90 90 90 90 90 90
EBITDA (16) (42) (64) (105) 133 180 214 252
Title To quickly check to make sure prices are still the same filter the prices on the tab proj 3 to go in order 1 to 100 and this should match
[Optional comments]
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
Earnings statement
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015
000
Cost of Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($lbs) 2560 2814 3102 3450 11926
Volume (lbs) 12800 13400 14100 15000
Price 020 021 022 023
000
Natural gas ($Kcf) - Henry Hub 5502 5788 6075 6362 23727
Volume 960 1010 1060 1110
Price 573 573 573 573
000
Electricity ($MWh) - NY ISO 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($gal) - NY Harbour 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
000
General amp Admin Exp 9000 9000 9000 9000 36000
000
EBITDA 6115 7608 9186 10802 33710
Impact of hedging
all figures in millions (except prices)
Futures Used
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
Net exposure (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 207 404 530 771 1913
Volume (lbs) 1280 1340 1410 1500
Price 036 051 060 074
00 00 00 00 00
Natural gas 718 829 956 1008 168 250 348 372 1138
Volume 96 101 106 111
Price 75 82 90 91
00 00 00 00 00
Electricity 844 888 953 1125 221 266 331 502 1320
Volume 10 10 10 10
Price 844 888 953 1125
00 00 00 00 00
Diesel 385 416 407 453 26 39 12 38 114
Volume 160 168 176 185
Price 24 25 23 24
00 00 00 00 00
Aluminum 451 543 678 805 151 223 340 448 1162
Volume 16 17 18 19
Price 282 319 377 424
Total 2861 3361 3834 4507 773 1182 1561 2131 5647
Correlated Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum Total Net Exposure
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Full-Year
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Volume 12800 13400 14100 15000 960 1010 1060 1110 100 100 100 100 1600 1680 1760 1850 160 170 180 190
Simulated
5 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 48715
95 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145722
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
Mean
Net Exposure Net Portfolio Exposure
32380 47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739 32380
33948 42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789 33948
39948 55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685 39948
43542 60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103 43542
44977 68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 44977
48911 50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956 48911
49824 97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168 49824
52614 9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173 52614
54247 89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834 54247
54467 29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518 54467
56081 37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959 56081
56950 66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358 56950
58740 44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783 58740
59233 63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890 59233
60239 11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751 60239
60316 3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213 60316
61664 56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573 61664
65269 4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321 65269
65273 92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204 65273
65764 69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759 65764
65876 93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008 65876
66078 87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886 66078
66116 74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632 66116
66562 28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686 66562
66781 5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205 66781
68244 27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291 68244
69290 94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559 69290
69701 32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209 69701
70796 10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158 70796
71222 62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991 71222
71261 45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691 71261
72702 91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228 72702
73734 20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550 73734
73801 52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806 73801
74108 88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414 74108
74317 1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024 74317
74660 22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160 74660
74808 31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368 74808
74998 82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171 74998
75350 8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146 75350
75850 85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158 75850
76270 2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409 76270
78037 30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375 78037
78226 23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103 78226
81022 96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662 81022
82068 99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748 82068
82643 35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858 82643
87060 14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447 87060
87614 83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068 87614
89614 46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928 89614
90297 100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267 90297
91492 49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762 91492
92285 79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150 92285
92517 80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079 92517
92520 58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466 92520
93297 38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511 93297
93492 84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782 93492
94010 72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702 94010
95355 25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982 95355
95981 51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310 95981
96590 70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430 96590
96659 59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987 96659
98984 39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335 98984
99473 64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881 99473
100438 18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643 100438
104925 26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805 104925
106683 71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274 106683
107046 48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657 107046
108929 75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922 108929
109789 17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544 109789
109940 98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393 109940
111963 61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121 111963
113140 41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558 113140
114475 15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927 114475
115420 76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479 115420
115470 12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839 115470
115543 43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938 115543
115599 16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233 115599
115628 95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056 115628
115695 13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336 115695
116747 78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058 116747
118794 33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808 118794
120798 67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960 120798
122134 7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729 122134
123516 73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263 123516
125606 90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639 125606
126928 54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417 126928
128846 77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264 128846
128878 6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513 128878
130807 34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239 130807
140398 19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517 140398
142414 81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603 142414
143917 53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292 143917
145005 65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126 145005
145638 36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145638
147330 40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883 147330
149955 86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437 149955
167385 24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467 167385
178469 57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836 178469
195830 21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430 195830
Risk management portfolio
Futures Description
Sugar Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Natural gas Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Electricity Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Diesel Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Aluminum Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Gross exposure calculations
Gross Exposure
all figures in millions (except prices) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 442 543 651 801 186 261 341 456 1244
Volume (lbs) 1280 1340 1410 1500
Price 035 041 046 053
00 00 00 00 00
Natural gas 760 857 982 1141 210 278 375 505 1368
Volume (MMBTU) 96 101 106 111
Price 792 849 927 1028
00 00 00 00 00
Electricity 1004 1259 1384 1492 381 636 762 869 2649
Volume (MWh) 10 10 10 10
Price 1004 1259 1384 1492
00 00 00 00 00
Diesel 473 537 634 689 113 160 239 274 786
Volume (gal) 160 168 176 185
Price 295 320 360 373
00 00 00 00 00
Aluminum 406 483 569 659 106 164 231 302 803
Volume (tons) 16 17 18 19
Price 2540 2843 3160 3469
Total 3085 3679 4220 4783 996 1500 1947 2407 6850
Sales Pricing and Volumes
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015 016
000
Cost of Commodity Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($) 2560 2814 3102 3450 11926
Volume (lb) 12800 13400 14100 15000
($lb) 020 021 022 023
000
Natural gas ($) 5502 5788 6075 6362 23727
Volume (MMBTU) 960 1010 1060 1110
Price ($MMBTU) 573 573 573 573
000
Electricity ($) 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($l) 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum ($) 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
Correlations
Correlated random shocks generated from correlation matrix
Correlations Sugar Natural gas Electricity Diesel Aluminum
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Sugar Q1 100 099 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q2 099 100 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q3 099 099 100 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q4 099 099 099 100 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Nat Gas Q1 065 065 065 065 100 099 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q2 065 065 065 065 099 100 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q3 065 065 065 065 099 099 100 099 094 094 094 094 086 086 086 086 069 069 069 069
Q4 065 065 065 065 099 099 099 100 094 094 094 094 086 086 086 086 069 069 069 069
Electricity Q1 055 055 055 055 094 094 094 094 100 099 099 099 082 082 082 082 071 071 071 071
Q2 055 055 055 055 094 094 094 094 099 100 099 099 082 082 082 082 071 071 071 071
Q3 055 055 055 055 094 094 094 094 099 099 100 099 082 082 082 082 071 071 071 071
Q4 055 055 055 055 094 094 094 094 099 099 099 100 082 082 082 082 071 071 071 071
Diesel Q1 047 047 047 047 086 086 086 086 082 082 082 082 100 099 099 099 066 066 066 066
Q2 047 047 047 047 086 086 086 086 082 082 082 082 099 100 099 099 066 066 066 066
Q3 047 047 047 047 086 086 086 086 082 082 082 082 099 099 100 099 066 066 066 066
Q4 047 047 047 047 086 086 086 086 082 082 082 082 099 099 099 100 066 066 066 066
Aluminum Q1 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 100 099 099 099
Q2 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 100 099 099
Q3 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 100 099
Q4 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 099 100
Column C C C C G G G G - 0 K K K - 0 O O O S S S S
Indirect C_Prices - 0 - 0
Correlated Sugar Natural gas Electricity Diesel Aluminum
N(01) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1 (108) (092) (082) (107) (047) (078) (057) (062) (015) (003) (008) (027) 041 052 041 022 052 051 036 039
2 031 (007) (006) (003) (015) (014) (012) (009) (033) (017) (021) (009) 005 005 026 (009) (087) (103) (079) (074)
3 (176) (167) (159) (155) (092) (079) (090) (098) (058) (061) (057) (069) (009) 018 007 002 (124) (101) (128) (121)
4 (134) (119) (112) (122) (046) (044) (043) (052) (066) (052) (037) (044) 016 009 (016) (003) (106) (116) (111) (094)
5 (075) (080) (049) (077) (063) (028) (057) (072) (045) (056) (051) (042) 011 010 021 032 (135) (127) (130) (123)
6 082 082 072 095 137 110 115 132 119 098 119 143 095 104 085 108 092 107 098 107
7 035 050 038 044 098 108 105 100 140 129 127 131 123 119 119 139 (024) (022) (013) (010)
8 (056) (044) (076) (066) (017) (033) (033) (045) (009) (007) 003 016 (091) (074) (102) (071) 078 068 080 058
9 (025) (015) (023) (027) (104) (127) (109) (105) (135) (150) (144) (166) (149) (148) (165) (151) (130) (126) (134) (131)
10 126 116 115 112 (120) (097) (107) (114) (054) (046) (052) (053) (107) (099) (125) (099) (117) (120) (125) (135)
11 (166) (182) (173) (174) (090) (083) (095) (089) (061) (070) (065) (060) (066) (048) (061) (084) 006 (013) (002) (006)
12 014 014 034 034 074 062 073 046 131 148 164 155 003 (003) 002 (035) 006 (003) 000 010
13 034 028 011 035 142 130 113 120 118 112 086 091 060 030 044 052 085 081 088 084
14 (048) (059) (063) (068) 033 026 020 050 021 017 019 023 001 (012) 009 005 089 097 075 099
15 079 091 090 091 083 082 073 081 096 086 093 081 107 112 115 096 047 038 030 024
16 058 071 034 059 116 117 122 103 064 084 084 092 081 080 068 108 093 092 082 070
17 (046) (041) (043) (048) 099 112 121 101 138 111 102 126 031 049 027 052 (019) (030) (033) (045)
18 030 052 025 024 060 078 068 059 073 093 069 070 031 033 026 030 (020) (037) (024) (026)
19 (034) (031) (025) (026) 146 114 124 151 166 173 175 161 121 116 108 097 222 227 201 212
20 (119) (138) (136) (114) (009) (009) 003 (012) (048) (021) (028) (040) 073 048 037 039 (028) (009) (003) (044)
21 092 081 085 085 236 228 257 241 229 231 230 239 259 264 269 263 098 082 075 097
22 047 052 031 042 (014) (030) (006) (016) (028) (027) (041) (031) (035) (040) (014) (031) (122) (127) (135) (134)
23 (043) (047) (046) (069) (021) (019) (014) (044) (005) 020 005 (006) (032) (025) (021) (012) 075 060 052 051
24 082 083 064 089 195 192 202 195 198 209 195 198 138 109 128 113 201 194 206 208
25 039 030 035 039 042 037 048 054 067 069 066 059 (050) (065) (051) (061) 032 038 040 033
26 130 157 113 138 057 052 036 048 076 085 063 076 (053) (058) (061) (052) 009 004 (012) 004
27 (063) (063) (067) (046) (041) (045) (053) (035) 012 015 004 (003) (140) (136) (152) (120) (116) (127) (101) (101)
28 (051) (067) (068) (058) (075) (063) (051) (068) (056) (050) (053) (068) (031) (025) (019) (053) (022) (005) (014) (018)
29 (165) (163) (176) (155) (115) (130) (143) (128) (065) (067) (081) (085) (128) (105) (126) (127) (058) (047) (031) (050)
30 (029) (031) (029) (032) (007) (016) (019) (003) 010 018 005 012 001 017 007 (007) (077) (089) (071) (081)
31 (040) (055) (058) (043) (004) (009) (010) (041) (007) (023) (027) (028) (065) (082) (086) (075) 078 068 065 089
32 123 098 102 105 (047) (059) (069) (079) (101) (106) (094) (097) (069) (074) (072) (089) (125) (131) (115) (122)
33 057 052 066 070 082 074 077 075 085 082 081 079 079 090 068 091 245 239 238 237
34 174 156 162 156 142 131 136 135 081 071 075 086 143 133 150 126 046 052 053 071
35 (121) (127) (111) (122) (004) (005) 018 (009) 051 059 046 032 (021) 003 (018) (008) 034 046 012 013
36 187 205 195 203 123 123 130 120 105 097 101 120 046 051 025 042 262 246 265 270
37 (019) (031) (029) (029) (106) (097) (106) (108) (103) (098) (084) (097) (090) (092) (107) (106) (199) (187) (190) (194)
38 080 080 081 069 018 036 (001) 014 (002) (022) (018) (017) 074 071 073 069 109 102 092 107
39 040 053 044 058 085 065 070 063 020 040 029 023 029 053 042 047 076 059 077 084
40 085 100 108 089 134 123 111 133 158 169 164 168 151 138 173 169 149 174 152 150
41 082 097 061 076 101 085 105 108 092 079 093 076 111 111 112 113 (045) (064) (044) (042)
42 (123) (134) (135) (147) (263) (271) (274) (254) (270) (276) (279) (268) (210) (223) (208) (227) (245) (244) (236) (255)
43 (016) (018) (018) (034) 100 092 094 115 122 134 110 112 115 109 102 111 027 024 035 026
44 (048) (052) (042) (035) (120) (122) (121) (119) (085) (098) (098) (095) (112) (115) (120) (132) (006) 005 001 (000)
45 069 067 059 054 (073) (073) (070) (067) (057) (073) (070) (080) (033) (026) (039) (057) (045) (040) (018) (017)
46 005 011 013 005 014 048 036 037 031 036 041 047 (043) (018) (052) (039) 030 037 050 024
47 (235) (256) (232) (222) (247) (231) (242) (221) (232) (219) (219) (235) (272) (277) (282) (278) (312) (301) (287) (275)
48 143 131 119 126 084 106 097 108 (004) 017 025 021 087 072 067 071 (004) 001 (000) (023)
49 (063) (045) (054) (035) 023 021 028 027 075 105 080 081 (048) (052) (049) (045) (006) 001 002 (004)
50 (055) (060) (066) (079) (129) (132) (138) (130) (154) (145) (127) (155) (157) (121) (133) (121) (205) (205) (217) (195)
51 039 028 044 031 052 056 043 052 030 025 046 023 035 034 022 042 079 079 067 081
52 (087) (075) (088) (067) (009) 002 (016) (002) 007 (011) (013) 000 (087) (077) (079) (081) (001) 016 (007) 004
53 (032) (024) (025) (035) 149 156 161 156 156 170 148 179 148 147 170 141 170 188 173 191
54 186 179 168 155 124 117 143 121 053 066 061 058 106 101 095 112 093 084 097 095
55 (135) (144) (137) (150) (190) (192) (201) (188) (170) (167) (161) (174) (185) (199) (201) (171) (296) (281) (273) (299)
56 (052) (051) (058) (040) (075) (083) (084) (087) (111) (124) (115) (109) (019) (009) 002 (019) (046) (047) (058) (039)
57 173 174 186 206 185 163 174 165 179 199 202 204 095 113 095 113 240 264 251 239
58 076 087 072 084 039 077 056 072 (005) 026 004 024 008 (004) (018) (002) (056) (058) (067) (061)
59 (072) (037) (056) (072) 059 073 063 057 048 051 060 051 074 102 100 080 033 037 022 034
60 (109) (093) (104) (106) (170) (175) (156) (156) (192) (185) (165) (178) (238) (234) (237) (243) (122) (127) (109) (150)
61 044 048 032 047 104 106 096 106 063 077 086 081 102 092 089 080 079 079 059 054
62 (105) (101) (086) (094) 009 (006) (009) (007) (027) (025) (023) (025) 026 015 030 041 (161) (157) (166) (184)
63 (095) (103) (105) (122) (077) (072) (095) (081) (123) (108) (120) (115) (077) (075) (081) (075) 020 018 028 018
64 (079) (070) (083) (086) 074 070 060 069 062 065 077 059 121 103 119 133 016 011 (005) 017
65 096 093 106 097 146 131 129 142 147 140 150 146 169 166 156 154 176 197 186 175
66 (049) (046) (037) (042) (084) (088) (081) (084) (110) (125) (112) (120) (149) (140) (151) (124) (042) (051) (064) (085)
67 061 045 052 064 096 126 115 098 107 100 105 103 157 142 150 155 009 034 031 029
68 (085) (114) (100) (117) (180) (194) (192) (178) (165) (186) (182) (184) (135) (123) (134) (140) (131) (134) (116) (157)
69 (084) (062) (057) (060) (071) (050) (052) (053) (104) (110) (109) (100) 028 002 031 008 (003) (012) (008) (004)
70 (002) 011 007 002 058 065 058 057 042 027 035 034 069 050 045 037 066 076 106 097
71 023 024 035 027 075 066 071 081 095 084 089 077 039 040 032 039 076 096 076 076
72 (005) (026) (008) (013) 061 048 062 036 027 026 034 017 121 143 116 129 (056) (025) (024) (015)
73 048 050 056 053 118 111 110 109 097 090 099 083 203 196 178 200 072 102 084 074
74 (119) (122) (110) (137) (040) (041) (045) (036) (033) (031) (018) (030) (071) (076) (086) (093) (044) (057) (050) (028)
75 114 095 103 110 098 087 111 092 026 041 026 023 109 106 108 120 027 029 017 023
76 057 048 022 022 085 080 076 090 086 072 090 077 167 169 163 169 098 084 086 103
77 190 171 152 155 112 132 124 105 111 085 105 108 040 053 065 059 046 074 069 075
78 077 059 048 077 080 079 090 080 111 120 103 119 089 077 070 073 059 064 042 045
79 118 100 116 107 005 (002) 006 011 010 031 016 013 (033) (034) (056) (059) 046 050 062 058
80 033 017 045 032 047 053 045 050 (010) 004 (012) (001) 066 093 082 077 045 034 044 048
81 182 197 190 205 085 101 107 099 114 122 103 125 081 073 071 071 197 192 209 220
82 076 081 062 078 (080) (086) (091) (084) (093) (085) (087) (086) (006) (014) (000) 009 082 069 074 061
83 156 153 147 166 006 (004) 020 003 (019) (018) (026) (025) (052) (052) (037) (048) (178) (171) (159) (160)
84 017 (010) (000) 003 048 058 066 040 042 051 037 048 020 052 037 030 006 006 (001) (000)
85 006 007 006 (000) 009 (020) (017) (013) 010 005 007 023 (045) (064) (063) (069) (131) (148) (138) (135)
86 078 080 076 077 162 150 131 145 144 154 144 151 281 273 262 269 091 085 107 097
87 (034) (019) (010) (011) (080) (060) (085) (074) (086) (087) (093) (097) (024) (034) (033) (036) (001) 001 010 017
88 (070) (046) (064) (058) (028) (040) (049) (046) (009) 005 (021) (021) 038 056 036 033 (045) (052) (050) (073)
89 (031) (037) (025) (013) (099) (097) (094) (098) (097) (107) (096) (084) (158) (167) (144) (163) (221) (230) (232) (238)
90 123 130 136 128 057 060 088 099 103 109 105 089 112 116 115 112 133 118 133 122
91 (146) (126) (133) (132) (037) (026) (029) (035) (010) (020) (038) (029) 000 (024) (020) (022) 059 068 077 069
92 (001) 007 (009) 018 (073) (053) (058) (058) (065) (078) (068) (070) (048) (052) (051) (052) (112) (143) (127) (123)
93 (226) (202) (225) (216) (056) (079) (073) (055) (020) (046) (045) (052) (011) (017) (014) (016) 027 021 039 037
94 (131) (113) (112) (115) (025) (019) (023) (024) (021) (013) (037) (047) (034) (005) (048) (023) (037) (042) (041) (042)
95 (087) (084) (091) (090) 066 068 041 072 116 108 119 121 152 144 136 136 152 180 159 168
96 (042) (035) (040) (038) (018) (001) (010) (018) 011 004 025 008 031 040 035 039 (011) (013) (012) (022)
97 (151) (157) (167) (162) (122) (147) (143) (134) (141) (132) (114) (134) (093) (074) (093) (093) (108) (119) (109) (132)
98 096 101 091 109 097 102 101 107 070 074 071 040 097 098 089 099 (062) (089) (089) (077)
99 033 028 029 021 (008) (017) (022) (009) (001) (010) 018 (002) 027 008 007 033 (038) (031) (030) (006)
100 (055) (051) (066) (067) 011 011 029 005 018 022 034 038 091 085 091 088 073 071 087 075
Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Simulated
5 011 009 007 006 407 327 289 267 3441 2649 2197 1553 165 147 131 120 1348 1197 1063 958
95 035 041 046 053 792 849 927 1028 10040 12591 13844 14919 295 320 360 373 2540 2843 3160 3469
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024
2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409
3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213
4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321
5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205
6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513
7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729
8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146
9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173
10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158
11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751
12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839
13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336
14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447
15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927
16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233
17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544
18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643
19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517
20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550
21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430
22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160
23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103
24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467
25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982
26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805
27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291
28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686
29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518
30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375
31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368
32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209
33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808
34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239
35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858
36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238
37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959
38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511
39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335
40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883
41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558
42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789
43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938
44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783
45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691
46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928
47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739
48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657
49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762
50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956
51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310
52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806
53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292
54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417
55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685
56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573
57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836
58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466
59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987
60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103
61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121
62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991
63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890
64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881
65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126
66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358
67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960
68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081
69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759
70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430
71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274
72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702
73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263
74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632
75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922
76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479
77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264
78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058
79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150
80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079
81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603
82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171
83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068
84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782
85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158
86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437
87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886
88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414
89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834
90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639
91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228
92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204
93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008
94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559
95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056
96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662
97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168
98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393
99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748
100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1
1
185947613236 209700016612 381289955056 113496909508 105940968217
261410846662 278202957602 636440926272 159777507841 164064511715
340758878594 374684278258 761658671012 238893166595 230844682326
456070302878 505106365162 869191059145 27405034829 302394276185
1 1 1
2 2 2
3 3 3
4 4 4
2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure
Sugar Sugar Sugar Sugar Sugar Sugar Sugar
Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas
Electricity Electricity Electricity Electricity Electricity Electricity Electricity
Diesel Diesel Diesel Diesel Diesel Diesel Diesel
Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum
1
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
USER INPUTS RISK PROFILE OUTPUTS
Date 1112 5 Holistic commodity profile and impact on earnings (baseline 95 confidence band 5 confidence band)
Price and Volatility Base 5th -ile 95th -ile
Q1 Q2 Q3 Q4 2012 Un hedged Hedged Impact Un hedged Hedged Impact
Price Volatility Price Volatlity Price Volatility Price Volatility of hedge of hedge
Sugar ($lbs) $ 020 70 $ 021 70 $ 022 70 $ 023 70 Sales 2069 2069 2069 - 0 2069 2069 - 0
Natural gas ($MMBTU) $ 573 48 $ 573 48 $ 573 48 $ 573 48 Cost of Goods Sold
Electricity ($MWh) $ 6227 69 $ 6227 69 $ 6227 69 $ 6227 69 Fixed Costs 480 480 480 - 0 480 480 - 0
Diesel ($gal) $ 224 37 $ 224 37 $ 224 37 $ 224 37 Sugar 119 58 58 - 0 311 311 - 0
Aluminum ($KTon) $ 1878 32 $ 1878 32 $ 1878 32 $ 1878 32 Natural gas 237 112 112 - 0 351 351 - 0
Electricity 249 87 87 - 0 381 381 - 0
Exposure risk level 95 Diesel 155 101 101 - 0 166 166 - 0
Aluminum 131 91 91 - 0 248 248 - 0
Commodity volumes (in millions) Variable Costs 892 450 450 - 0 1456 1456 - 0
Q1 Q2 Q3 Q4 Total COGS 1372 930 930 - 0 1936 1936 - 0
Sugar (lbs) 1280 1340 1410 1500 Gen amp Admin Exp 360 360 360 - 0 360 360 - 0
Natural gas (MMBTU) 96 101 106 111 EBITDA 337 779 779 - 0 (228) (228) - 0
Electricity (MWh) 10 10 10 10
Diesel (gal) 160 168 176 185
Aluminum (KTon) 16 17 18 19 4 Net commodity exposure 2012 by commodity (in millions $)
Other financial statement assumptions
Q1 Q2 Q3 Q4
Sales volume (in millions) 3200 3360 3528 3704
Sales price ($) 015 015 015 015
Fixed costs ($ millions) 120 120 120 120
GampA Exp ($ millions) 90 90 90 90
Hedge Selection
Futures
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
3 Gross commodity exposure 2012 (in millions $)
a) By quarter b) By commodity
All exposures reported as the difference between the 95th percentile highest exposure and the expected baseline gross commodity exposure
2 Estimated commodity volumes for 2012 by quarters (variable units in millions $)
1 Commodity price projections (baseline 95 confidence band 5 confidence band)
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
Read Me - File Overview
Page 5: Commodity Risk Management - Oliver Wyman

1 COMMODITY PRICES AND VOLATILITY

After a brief respite in 2009 the volatility that has come to define many commodity markets

roared back in the latter half of 2010mdashmuch to the dismay of businesses for whom oil

industrial metals and other raw materials comprise a significant share of their costs Crude

oil cracked the psychological barrier of $100 per barrel major food price indexes reached

record highs in the first quarter of 2011 and base and industrial metals such as copper and

aluminum also reached new highs mdashcreating significant pain for buyers (see Exhibit 1) Even

where prices pulled back the cost of many commodities remained higher than beforemdashthe

result of structural shifts in supply and demand on both a local and global scale (For a closer

look at the causes of volatility see Exhibit 2)

These commodity shocks are not only cutting into corporate profits but are testing the

abilities of even the best businesses to plan for and invest in the future Already in 2011 the

CEOs of consumer-facing companies as diverse as PepsiCo Kraft Kimberly-Clark and Levirsquos

have said they expect commodity price inflation to pose a significant challenge to continued

earnings growth over the next several years Those in the middle of the value chain have a

tougher challenge as they cannot easily raise prices to recover lost margins Few expect this

storm to pass quickly The 2011 World Economic Forum Global Risks Survey found corporate

executives in agreement that a key risk they face in the coming decade is extreme volatility in

energy and other commodity prices1

1 World Economic Forum ldquoGlobal Risks 2011rdquo 6th edition January 2011 Oliver Wyman was a contributor to that report

EXHIBIT 1 COMMODITY PRICES ARE SOARING INDEX

8

12

10

6

4

2

0

14

MULTIPLE OF INITIAL PRICEREAL PRICE HISTORY

1987 1991 1995 2003 20111999 2007

Crude Oil

Copper

Wheat

source Datastream 2011

Copyright copy 2011 Oliver Wyman 3

ExhIBIT 2 WhY ThE VOLATILITY

CAUSE DESCRIPTION

Erratic weather Catastrophic weather events have affected production (2009 drought in Australia affected global wheat prices)

Emerging markets Growth in emerging markets has increased demand for food energy and raw materials Global food output will have to rise 70 by 2050 to meet demand while global energy demand is predicted to increase by 36 between 2008-2035

Speculation Emergence of commodities as an investment class (development of commodities-based ETFs)

Infrastructure spending Deteriorating infrastructure in developed countries and a lack of infrastructure in emerging economies hampers the physical flow of commodities

Supply-country strategies Development of market-distorting trade policies (Russian wheat export ban followed crop shortfall in 2010)

Purchasing-country strategies

Countries developing more sophisticated buying capabilities (Korea opened a grain-trading office in Chicago in 2011)

Food and Agriculture Organization of the United Nations World Summit on Food Security November 2009

International Energy Agency World Energy Outlook 2010

This volatility could persist for years particularly given that governments appear willing to disrupt or intervene in markets including halting exports These collective forces have created pressure for corporations to develop strategies to mitigate this growing risk While companies in agri-processing oil refining and wholesale electricity generation have developed formal approaches to trading and risk management programs the majority of companies need to do moremdashmuch more

Given the likelihood of further volatility in commodity prices every company must adopt an analytic risk framework based on a clear understanding of its exposure This paper offers guidance on developing a plan for managing commodity risks that draws on the best practices of companies from around the world The approach is built around the three pillars of a ldquobest practicesrdquo program governance infrastructure and analytics The paper provides an in-depth review of the role of analytics and outlines a new systematic approach to managing commodity risk illustrated through case studies

ONE COMPANYrsquoS MOVES TO TAME ITS VULNERABILITY TO VOLATILITY

A leading industrial company failed to deliver its projected earnings due to a decline in the profitability of non-core energy activities To measure the companyrsquos total exposure to energy volatilitymdashand quantify the potential effect on future profits it needed to develop an integrated energy risk profile In other words the company needed the ability to aggregate the energy exposure of each business unit and evaluate the effectiveness of existing mitigation efforts before it could truly understand net exposure

The company analyzed commodity volatilities and correlations to produce a probabilistic analysis and derive ldquoEPS-at-riskrdquo estimates demonstrating the portion of earnings that were vulnerable to these price volatilities It then adopted a risk assessment tool capable of performing scenario analysis linked to alternative market states and specific events integrating this discipline by training both operational and financial staff in Treasury Procurement Planning and Operations This helped the company to gain insights into the aspects of its energy exposure that were most sensitive to price movements under different future states and thus were targets for mitigation efforts

Copyright copy 2011 Oliver Wyman 4

2 COMMODITY RISK MANAGEMENT FRAMEWORK

DEVELOPING A COMMODITY RISK GOVERNANCE PROGRAM

A crude oil producermarketer in Eastern Europe was recently seeking to enhance margins by creating regional physical trading capability First the Board required stakeholder alignment on a multi-dimensional risk appetite statement to guide risk and scale considerations High level loss limits were linked to the risk appetite and from these ldquodesk-levelrdquo limits were calculated By codifying the risk limits in policies the equity usage growth targets and working capital requirements were made explicit and served to support all later decisions on the business expansion plan

A structured commodity risk management framework is built around three pillars

Governance Infrastructure and Analytics Together these pillars support both short-

term tactical decisions and long-term strategic initiatives (see Exhibit 3)

GOVERNANCE

The first pillar of this framework is Governance in which an organization identifies the main

objectives of its commodity risk management program Companies first create a risk

appetite statement In this critical step an organization defines its risk tolerance and

aligns it with its broader performance objectives The risk appetite statement thus

establishes the basis of the organizationrsquos risk limits

ExhIBIT 3 COMMODITY RISK MANAGEMENT FRAMEWORK

SHORT-TERM TACTICAL DECISION MAKING

LONG-TERM STRATEGIC DECISION MAKING

GOVERNANCE

bull Risk appetite and corporate objectivesbull Risk limits bull Risk policybull Delegations of Authoritybull Decision frameworks

INFRASTRUCTURE

bull Reportingbull Accountingbull ITsystemsbull Organizational designbull Human capital

ANALYTICS

bull Price forecastingbull Commodity exposure modeling bull Stress and scenario testingbull Hedge optimization

Copyright copy 2011 Oliver Wyman 5

INFRASTRUCTURE

The second pillar Infrastructure comprises the organizational structure systems and

human capital that companies need to measure and manage risks Organizations need to

assess whether they have the systems to capture and monitor the necessary data flows The

organizational structure is important because the volatility in commodity markets requires

discipline and a tight alignment between managing cost- and revenue-related risks across

the enterprise historically these functions worked independently but today that results in

inefficiencies or even conflicting actions by different internal teams human capital is also

key In some cases a corporation may not have the experience or capabilities to manage

commodity risks effectively While a company can outsource risk management to financial

institutions or other market participants it pays a significant premium for that service and

potentially forfeits any upside potential In the process it gives its ldquopartnerrdquo proprietary

information they can use to trade for solely their own benefit

INCREASING EFFECTIVENESS ThROUGh IMPROVED INFRASTRUCTURE

The effects of steady growth were slowly compromising the risk management effectiveness of a major North American crude and distillates marketertrader Reporting timeliness was eroding error rates were climbing and managementrsquos confidence in risk control was decreasing prompting a full review of the organizational design and processes This revealed that the risk control function (middle office) was still performing much of the trade reconciliation that trade details were transmitted by email and that most reporting was done through spreadsheets

Several straightforward organizational shifts and systems enhancements eliminated a number of bottlenecks and enabled risk reporting down to the cargo level by desk and counterparty

ANALYTICS

The third pillar of a commodity risk management program is Analytics Organizations

cannot assess their financial exposure to commodity price swings without robust analytical

tools These analytics (or ldquomodelingrdquo) platforms support decision making at every level

mdash by helping managers model the future paths of commodity prices conduct stress- and

scenario-testing and evaluate and optimize risk-return profiles using a range of price-risk

management strategies however analytical capabilities vary dramatically from firm to firm

Companies should take a sequential approach to developing a robust analytics framework

that will support commodity risk management

Copyright copy 2011 Oliver Wyman 6

ThINK LIKE A TRADER

Trading is viewed as a necessary evil by some corporations given the connotations of excessive speculation and risk-taking with derivatives (in this case commodity futures contracts) A recent review of annual reports reveals that many companies disclosing commodity positions caution that these are strictly for hedging purposes and that the company ldquodoes not hold financial derivative positions for the purposes of tradingrdquo

Whether they realize it or not many companies are in fact speculating on commodity pricesmdashon both the cost and revenue sides Procurement officers are tasked with getting the best price on purchases for the company and its business units So the procurement staff commonly enters into forward fixed-price arrangements to guarantee both supply and pricing Just like traders they have thus made a bet on future prices and concluded price risk management with that view in mind

Companies need to accept that the days of a ldquoset it and forget itrdquo approach to risk management are over That approach was fine when volatility was low and prices increased gradually over time Today companies need to empower designated executives to think like traders ldquoIs this a good or bad price Should I buy more or run down inventoryrdquo Since most companies arenrsquot traders by nature they need to create a trading playbook that is integrated with their overall game plans

First a company must understand its commodity risk profile This helps the organization

assess its net exposure to commodity pricesmdashand their inevitable volatilitymdashacross

business and customer segments Detailed analysis also allows the organization to then

develop long-term strategies to mitigate commodity-related risks

A commodity risk profile provides a common understanding for senior management and

a fact-based foundation for evaluating the effectiveness of risk-mitigation actions With

this knowledge management can determine if the companyrsquos current commodity risk

exposure is within its risk tolerance and whether it has communicated these expectations

to stakeholders This analysis also helps the management team evaluate different risk

management strategies It promotes risk mitigation at the portfolio level which helps

reveal any risks lurking in individual business units or departments In short this analysis

will allow the company to optimize risk-return positioning

ThE NET ExPOSURE ChALLENGE

Determining true net exposure requires consideration of several factors gross commodity exposure existing risk mitigations foreign exchange flows demand sensitivity and the interactions among these This can be challenging for an enterprise with wide-spread operations or multiple product lines Typically each factorrsquos net impact at the corporate level is first assessed and any offsets across factors can then be accounted for

Copyright copy 2011 Oliver Wyman 7

To manage commodity price risks in ways that are consistent with broader corporate

objectives companies need a robust set of analytic tools to calculate the current exposure

Exhibit 4 offers a six-step analytical approach that companies can use to determine the

effect of commodity price risks and mitigation efforts on key financial metrics

The first step in the analytical framework requires management to build a forecast

of commodity prices using simulations or other techniques The company should

complement these forecasts with an analysis of alternative price outcomes based on ldquostress

eventsrdquo to understand fully how prices may evolve

In the second step the management team estimates the volume of future commodity

purchases across the enterprise In Step 3 this demand forecast is combined with

price projections to determine the firmrsquos gross commodity exposure In Step 4 the risk

management strategies already in place are identified Then in Step 5 these are applied

against the gross exposure to generate a ldquonet commodity exposurerdquo for the enterprise The

process ends with Step 6 the creation of a holistic company-wide risk profile ndash with the

sensitivities in price projections identified in Step 1 used to explore the potential impact on

EBITDA debt covenants and other financial metrics

Copyright copy 2011 Oliver Wyman 8

ExhIBIT 4 CREATING A hOLISTIC COMMODITY RISK PROFILE

bull Use analytic engines to simulate potential commodity price pathways (data driven)

bull Incorporate market context and paradigm shiftsscenario analysis ( judgment driven)

bull Integrates historical patterns market intelligence and fundamental analysis

bull Unifies disparate views of expected and highlow commodity price scenarios across organization

bull Incorporates interrelationships and correlations between commodities and currencies

STEP ANALYSIS BENEFITS

bull Centralizes commodity requirements across business units and geographies

bull Enables testing of alternative commodity purchase requirements using price elasticity analysis

bull Determine expected commodity purchase volume based on sales expectations

bull Gives context of expected commodity exposure compared to PampL and other risks

bull Accounts for ldquonatural hedgesrdquo in the portfolio

bull Shows shifts due to changes in business mix commodity price expectations

bull Calculate expected commodity exposure (eg price multiplied by volume)

bull Brings together and coordinates dierent functions of the organization (eg Strategy Procurement Treasury) and geographies

bull Sets understanding of risk management options for key commodity exposures

bull Define options for managing commodity price risk

bull Centralize risk management options undertaken across organization

bull Helps management assess the eectiveness of the risk management portfolio vs desired exposure

bull Provides understanding of how commodity price risk flows through the organization

bull Calculate exposure after incorporating current risk management portfolio

bull Enables objective evaluation and comparison of a range of risk management strategies

bull Promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at individual business unit or department level

bull Provides view of the earnings impact from commodity prices at dierent levels of probability

bull Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

COMMODITY PRICEPROJECTIONS

SALES PRICINGANDPURCHASE VOLUMES

GROSS EXPOSURE

RISK MANAGEMENTPORTFOLIO

NET EXPOSURE

HOLISTIC COMMODITYRISK PROFILE

Source Oliver Wyman

Copyright copy 2011 Oliver Wyman 9

IMPROVING COMPETIVENESS WITh A STRATEGIC RESPONSE TO COMMODITY VOLATILITY

A global food ingredient processor was losing sales to much smaller competitors solely because those firms were much more responsive to the highly volatile product price By analyzing the impacts of its forward sales process and market price uncertainty a new strategy was developed to migrate customers to shorter term contracts and to begin linking prices to market indices ndash which immediately improved the firmrsquos competitive positioning

STRATEGIES FOR MANAGING COMMODITY RISK

With a well-defined risk profile and an understanding of its ability to manage risk a

company can build a plan that matches its net exposure to commodities with its tolerance

for risk To manage commodity prices in the short term companies generally have three

tools at their disposal

bull Product pricingmdash identifying customer segments where the company has the ability to

raise prices or create pricing structures that mitigate risk

bull Procurement contract structuringmdashdeveloping innovative risk-sharing contracts with

suppliers

bull Financial hedgingmdashusing financial instruments for hedging to reduce overall

risk exposure

The effectiveness of these short-term strategies depends on the size of the organizationrsquos

exposure to a given commodity mdash and the commodity itself For example financial hedging

works best in commodity markets that are liquid (eg energy products such as crude oil and

natural gas or agricultural products such as wheat and corn) Meanwhile passing higher

commodity costs to customers through price increases is often ineffective in competitive

markets such as consumer products however when commodity cost increases are

significant and widespread pass-through pricing might be more viable Some companies

have taken creative approaches to raising prices For instance a number of consumer

products companies have reduced the volume of productmdashwhile keeping the same

package sizemdashto maintain margins in the face of higher commodity costs Other firms have

substituted cheaper ingredients to lower their net product costs

Over the long term volatility in commodity prices affects the behaviors of consumers

companies and their suppliers In response some companiesmdashand even countries--have

embedded commodity risk strategies into their long-term strategic plans

Copyright copy 2011 Oliver Wyman 10

CONCLUSION

Large and sustained commodity price swings are reshaping whole industries This means

that commodity risk management can no longer be considered the sole responsibility of

the procurement or finance staff With rising commodity prices affecting both the short-

term earnings and long-term strategies it is imperative that C-level executives develop a

deeper understanding of how to mitigate these risks Developing a structured commodity

risk management program built around the components outlined here is crucial Given

the new realities of higher prices and more volatility in commodities organizations must

integrate commodity risk management into their day-to-day operations They also must

build it into their long-term strategies to ensure the viability of the firm itself

ABOUT THE AUTHORS

MICHAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman

Michael J Denton PhD is a New York based Partner in Oliver Wymanrsquos Global Risk amp Trading practice with specialized experience in

energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market

risk modeling portfolio dynamics and risk-based decision frameworks Recent projects have focused on due diligence evaluations

competitive contracting and counterparty credit risk mitigation

ALEX WITTENBERG

Partner Global Risk amp Trading Oliver Wyman

Alex is the Managing Partner of Oliver Wymanrsquos Global Risk Center and has over 20 years of cross-industry experience in risk management

advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision-making and financial performance designing

risk governance for Boards and Management and developing corporate risk monitoring mitigation and transfer frameworks

Copyright copy 2011 Oliver Wyman 11

Copyright copy 2011 Oliver Wyman All rights reserved This report may not be reproduced or redistributed in whole or in part without the written permission of Oliver Wyman and Oliver Wyman accepts no liability whatsoever for the actions of third parties in this respect

The information and opinions in this report were prepared by Oliver Wyman

This report is not a substitute for tailored professional advice on how a specific financial institution should execute its strategy This report is not investment advice and should not be relied on for such advice or as a substitute for consultation with professional accountants tax legal or financial advisers Oliver Wyman has made every effort to use reliable up-to-date and comprehensive information and analysis but all information is provided without warranty of any kind express or implied Oliver Wyman disclaims any responsibility to update the information or conclusions in this report Oliver Wyman accepts no liability for any loss arising from any action taken or refrained from as a result of information contained in this report or any reports or sources of information referred to herein or for any consequential special or similar damages even if advised of the possibility of such damages

This report may not be sold without the written consent of Oliver Wyman

wwwoliverwymancom

Oliver Wyman is a leading global management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development Oliver Wymanrsquos Global Risk Center is dedicated to analyzing increasingly complex risks that are reshaping industries governments and societies Its mission is to assist decision makers in addressing risks by combining Oliver Wymanrsquos rigorous analytical approach to risk management with leading thinking from professional associations non-governmental organizations and academic institutions For further information please visit wwwoliverwymancomglobalriskcenter

The Association for Financial Professionals (AFP) headquartered outside Washington DC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFP is the daily resource for the finance profession

For more information please contact

MIChAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman +16463648423 michaeldentonoliverwymancom

ALEx WITTENBERG

Partner Global Risk amp Trading Oliver Wyman +16463648440 alexwittenbergoliverwymancom

BRIAN T KALISh

Director Finance Practice Lead +13019616564 bkalishafponlineorg

Oliver Wyman
File Attachment
Commodity volatilitypdf

intRoduCtion

given the current and future trends in commodity prices and volatility every company must

better understand its true commodity exposure Companies can often be squeezed by

rising and volatile commodity input prices that cannot be passed along to customers in

their entirety A commodity risk management program can help not all organizations can

or should adopt the sophisticated mechanisms of a pure commodity business however

most organizations particularly those in the middle of the value chain can improve their

commodity risk analytics

exhibit 1 CompAnies ARe ChAllenged by Rising Commodity volAtility

Output prices

bull Competitive pressuresbull Limited ability to pass

through higher costs

COMPANY

bull Understand current forecasts (starting position and price curve)

bull Trace impact of price volatility to earnings

bull Align commodity risk management program strategy with corporate objectives

Commodity risk management program strategy instruments etc

Input prices

bull Increasing volatilitybull Increasing absolute pricebull Breakdown in correlations

the starting point is to understand the companyrsquos holistic commodity risk profile using

analytics and modeling tools A holistic commodity risk profile helps the organization assess

its individual and net exposure to commodity pricesmdashand the inevitable volatilitymdashacross

business and customer segments on a forward-looking basis the profile provides a common

understanding for senior management and a ldquofact-basedrdquo foundation for evaluating the

effectiveness of current risk-mitigation actions and alternative risk management strategies

With this knowledge management teams can determine if current commodity risk exposure

is within the companyrsquos risk tolerance and communicate these expectations to stakeholders

it also helps to promote risk mitigation at the portfolio level by identifying the most

important drivers of overall risk and ensuring the capture of any offsetting risks that may be

present in short this analysis will allow the company to optimize risk-return positioning

this In Practice Guide provides an overview of a stepwise approach and analytic processes

necessary to develop an organizationrsquos net commodity exposure

Copyright copy 2012 oliver Wyman 3

six steps to deteRmining Commodity exposuRe And impACts

exhibit 2 provides an overview of a six-step analytical approach to determine the impact of

commodity price risks on key financial metrics

exhibit 2 six steps to CReAte A holistiC Commodity Risk pRoFile

6 HOLISTIC COMMODITY RISK PROFILE

5 NET EXPOSURE

4 RISK MANAGEMENT

PORTFOLIO

3 GROSS EXPOSURE

2 SALES PRICING AND COMMODITY

PURCHASE VOLUMES

1 COMMODITY PRICE

PROJECTIONS

STEPS

ANALYSISUse analytic engines to simulate potential commodity price pathways (data driven)

Incorporate market context and paradigm shifts scenario analysis (judgement driven)

Calculate expected commodity exposure (ie price multiplied by volume)

Define options for managing commodity price risk

Centralize risk management options undertaken across organization

Calculate exposure after incorporating current risk management portfolio

Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

Determine expected commodity purchase volume based on sales expectations

Copyright copy 2012 oliver Wyman 4

step 1 Commodity pRiCe pRojeCtions

CoRe AnAlysis user-defined baseline commodity prices and volatilities for a predefined set of commodity

inputs are incorporated into a standard simulation process to generate a distribution of

potential future price paths for each commodity

outputs A distribution of terminal price values for each commodity and time horizon

beneFits bull integrates historical patterns market intelligence and fundamental analysis

bull unifies views of commodity price scenarios across the organization

bull incorporates interrelationships and correlations between commodities and other price

risk factors (eg currencies)

the first step in the analytical framework requires

management to build assumptions for commodity

prices in the future using simulation or other techniques

the management team should complement these

forecasts with an analysis of alternative price outcomes

based on ldquostress eventsrdquo to understand fully how prices

may evolve

A simple price simulation model is presented in the

attached workbook (accessed through the pushpin

icon) the inputs for these simulations are located on

the tab labeled ldquointerfacerdquo under the heading ldquoprice

and volatilityrdquo the user defines base case prices and

volatilities for each commodity and forward period

(ie 1st quarter 2nd quarter and so on) the simulated

terminal value prices or outcomes for each commodity

and time horizon are shown on the tab labeled ldquoCmdy

price projrdquo each of these values reflects possible future

outcomes for commodity prices based on three factors

mean price volatility and time

the outputs of the simulations for each commodity are

summarized on the ldquointerfacerdquo page in the ldquoCommodity

price projectionsrdquo section in each case 5th and 95th

percentile outcomes are shown along with the baseline

price scenario over the course of the time period to

provide a richer understanding of possible outcomes

versus a single static forecast not surprisingly the

uncertainty of price forecasts grows with the increasing

time horizon

Copyright copy 2012 oliver Wyman 5

ReadMe

OverviewThe workbook has been prepared as a supplement to the In Practice Guide Six Steps to Assess Commodity Risk Exposure prepared by Oliver Wyman for AFP members and available at wwwafponlineorg The purpose of this workbook is to provide a simple tool for illustration purposes only to better understand and quantify commodity risk impacts on earmings The example focuses on a corporation which utilizes key commodities including natural gas diesel fuel sugar electricity and aluminum The components of the workbook include a primary Interface worksheet as well as support worksheets which house the calculations used to generate outputs on the Interface sheet Note that all user inputs and outputs are exclusively contained in the Interface sheet - all other sheets are for reference only Cells in white in the User Inputs block represent input cells (prices volatilities and hedge designation) Any changes in outputs are generated by using the Calculate button The Interface worksheet receives inputs and provides outputs for a probabilistic earnings forecast model and illustrates the impacts of commodity prices and volatilities on the earnings profile In particular each of the outputs reflects one of the steps in the 6 step process Outputs include(1) Commodity price projections - User-defined baseline commodity prices and volatilities for a pre-defined set of commodity inputs are incorporated into a standard simulation process to generate a distribution of potential future price paths for each commodity(2) Estimated commodity volumes - A predefined set of commodity volumes reflecting commodity demand over time(3) Gross commodity exposure - User-defined baseline prices are combined with simulated price paths and volumes to generate commodity exposure - measured as the difference between the 95th percentile worst case outcome and the baseline expected outcome (4) Net commodity exposure - Gross commodity exposure is adjusted for any financial or physical hedges for each of the commodities Hedges are assumed to lock in the commodity at the baseline expected price Price correlations between commodities are taken into account(5) Holistic commodity risk profile - Commodity exposures are combined with predefined estimates for revenues and other fixed costsSGA to illustrate both positive and negative impacts on the earnings profileKey assumptionsKey assumptions in the model include the following - A GBM process is used to simulate all of the commodity price paths - Correlations are predefined and fixed resulting in a static set of shocks used in the simulation process- User defined volumes and revenues are static- User defined prices and volatiliities are used as baseline assumptions for each quarter

Interface

Interface

REF
1

Cmdy Price Proj

Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
124418764137
1367693617635
2648580611483
786217932233
803244438443
5605736599794
124418764137
4238042982159
1367693617635
1589462370676
2648580611483
803244438443
786217932233
0
803244438443

Correlations

5
95
Mean
Sugar
01107087317
03452715728
02
00854552129
04050827214
021
00695646039
04616729635
022
0061034605
05340468686
023

Sales Pricing Volumes

Sugar
Natural gas
Electricity
Diesel
Aluminum

Gross Exposure

REF
EPS-at-risk
1

Risk Mgmt Portfolio

REF
EBITDA
1

Simulation Net Exposure

Diesel
Electricity
Natural gas
Crude oil
Price change
EPS impact
Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
1912572265773
1138117311849
1319658025171
11406289411
1162397994146
3734236225276
1912572265773
2596118913427
1138117311849
1276460888256
1319658025171
1162397994146
11406289411
0
1162397994146
5
95
Mean
Natural Gas
40688610903
79154680028
57310928297
32742030186
84855775584
57310928297
28887025303
92658501718
57310928297
26679947564
10281600624
57310928297

Net Exposure

5
95
Mean
Electricity
344141498094
1003995388957
622705433902
264877650618
1259146360173
622705433902
219723962528
1384358671012
6227
15528763279
1491896493046
622705433902

Fin Statement

5
95
Mean
Diesel
16508189205
29538139317
22444582473
14672697038
31955148416
22444582473
13074767002
36018057848
22444582473
11953891033
37258114813
22444582473

Price shock contuity

5
95
Mean
Aluminum
134769028369
254013105136
1878
119721652401
284279599425
187771063122
106347176349
316018108859
187771063122
95839609124
346925945325
187771063122

Graph support

Sugar
Natural gas
Electricity
Diesel
Aluminum
128
96
1
16
16
134
101
1
168
17
141
106
1
176
18
150
111
1
185
19
95ile
5ile
Earnings statement
-161353539101
1326569803377
611516334878
-421367106558
1798147036403
760774397443
-642177203637
2144399264928
918591594906
-1050874848209
2521161989213
1080153466317
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Correlations among commodity prices dictate the way in which commodity prices move together and are used to develop commodity price projections via simulation
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Commodity volumes may be determined by reviewing sales and demand planning forecasts Volumes may be aggregated when sourced from different business divisions or regions to arrive at a corporate-wide volume estimate
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Oliver Wyman
File Attachment
AFP-OW Commodity Risk Calculatorxls

step 2 sAles pRiCing And puRChAsing volumes

CoRe AnAlysis user-defined commodity volumes for the predefined set of commodities are coupled

with the distribution of price outcomes to support the determination of gross and net

exposures to commodities sales volumes and prices are incorporated into the pro forma

financial statement to understand earnings impacts from commodity exposures

outputs A predefined set of commodity volumes reflecting commodity demand over time

beneFits bull Centralizes commodity requirements across business units and geographies

bull enables testing of alternative commodity purchase requirements using price

elasticity analysis

in the second step the management team must

estimate the volume of future commodity purchases

across the enterprise the estimated volumes for inputs

are going to be directly linked to sales forecasts and

thus may be derived from product demand profiles

While the current model focuses on price uncertainty

it is important to recognize the presence of volume

uncertainty as well the static volumes are estimated

and then entered by the user on the tab labeled

ldquointerfacerdquo under the heading ldquoCommodity volumesrdquo

Assumptions for product sales volumes and prices are

also entered on the ldquointerfacerdquo tab for later use in the

holistic commodity risk profile assessment

step 3 gRoss exposuRe

CoRe AnAlysis user-defined baseline prices are combined with simulated price paths and volumes to

generate commodity exposure as measured by the difference between the 95th percentile

worst case outcome and the baseline expected outcome

outputs A firmrsquos gross commodity exposure given commodity price and volume

beneFits bull gives context of expected commodity exposure in comparison to pampl and other risks

bull shows changes over time due to either changes in business mix or changes in

commodity price expectations

in the third step the expected gross commodity

exposure is determined the 95th percentile worst case

commodity costs located on the tab labeled ldquogross

exposurerdquo are found by multiplying each commodity

volume by price gross exposure is determined by

summing the difference between the baseline cost and

the simulated 95th percentile worst case cost for each

individual commodity exposure Alternative percentiles

may be selected using the ldquoexposure risk levelrdquo input

value on the ldquointerfacerdquo tab

Copyright copy 2012 oliver Wyman 6

step 4 Risk mAnAgement poRtFolio

CoRe AnAlysis Risk management strategies currently in place are identified

outputs portfolio of existing risk management options (eg a set of commodity derivative hedges)

beneFits bull brings together and coordinates different functions of the organization (ie strategy

sales and marketing procurement treasury) and geographies

bull sets common understanding of risk management options for key commodity exposures

After determining gross commodity exposure the next

step in the process is to identify existing risk management

strategies the tab labeled ldquoRisk mgmt portfoliordquo has

a predefined list of risk management options available

which limit the management decision to financial

hedges that are assumed to lock-in the commodity at the

baseline expected price on the tab labeled ldquointerfacerdquo

under the heading ldquohedge selectionrdquo the user selects

ldquoyesrdquo or ldquonordquo for each commodity depending on whether

the firm plans to hedge that particular commodity it

is important to note that management of commodity

price risk is not limited only to financial hedging rather

it typically involves a combination of four broad levers

ndash each (or a combination thereof) may be effective as

illustrated in exhibit 3

besides recognizing current risk management

strategies this step also alerts management of other

potential risk mitigation options that may be available to

manage key commodities exposures

Copyright copy 2012 oliver Wyman 7

exhibit 3 Risk mAnAgement options

a Product Pricing (incl ldquoPass-throughrdquo)

b Procurement contracts

c Financial hedging

d Value chain integration long-term strategies

oVerView bull use market pricing

power to recover

change in input costs ndash

revenue management

bull balancesharetransfer

risk on purchases ndash

cost management

bull transfer risk to third

party through markets

structured deals

(Reinsurers also providing

capacity here)

bull up or downstream

integration eg

sourcing raw materials

retail trading

examPle strategies

bull employ cost pass-through

mechanisms (across

all or a portion of the

product portfolio)

bull Align price risk transfer

mechanisms with sales and

purchase contracts

bull include contract

mechanisms that mitigate

risk (eg price ceilings

floors price bands knock-

inknock-out clauses)

bull hedge price exposure

with swaps to set the price

or options or collars to

limit downside

bull Cross-hedge exposure

with contracts with

strong correlation to

underlying exposure

bull Acquire upstream company

to ensure supply and

diversify risk exposure

bull identify drivers that

contribute greatest

percentage to overall risk

Pros bull Raw materials costs fully

or partially passed through

to customers

bull stabilized margins and

earnings certainty

bull protection against price

moves and volatility

bull transition risk back

to supplier under

certain structures

bull protection against

feed stock price moves

and volatility

bull no impact on product

pricingsupplier contracts

bull holistic approach to cost

and margin management

bull diversification of

risk exposure

cons bull Customers may substitute

products or move to

lower priced competitor

brands in response to

product price

bull suppliers demand

premium (higher price)

for risk sharing

bull need to find

receptive suppliers

bull liquidity premium and

market non-availability

for many commodities

bull basis risk from

cross-hedging

bull Requires appropriate

capital and expertise

for joint ventures

or acquisitions

bull increased organizational

complexity

Copyright copy 2012 oliver Wyman 8

step 5 net exposuRe

CoRe AnAlysis predefined financial hedges and price correlations between commodities are applied to

gross commodity exposure to determine net commodity exposure for the enterprise

outputs A firmrsquos net commodity exposure after incorporating its current risk management portfolio

beneFits bull Accounts for natural hedges

bull provides management with an understanding of the effectiveness of the risk

management portfolio vs desired exposure

bull provides broad understanding of how commodity price risk flows through

the organization

While calculating net exposure management must also

take into account the correlation between commodity

prices price movements of one commodity may be related

to the price movement of other commodities thereby

increasing or decreasing exposure depending on the

relative strength of the relationship to account for this the

net exposure of the portfolio must be determined on the

tab labeled ldquosimulation net exposurerdquo the distribution

of terminal price values for each commodity and time

period (eg 1st quarter 2nd quarter etc) are multiplied

against volume for that same time period A distribution

of net exposures is generated and 5th and 95th percentile

outcomes for the portfolio series are selected

A companyrsquos net exposure to commodity price volatility is

shown on the tab labeled ldquonet exposurerdquo the predefined

volume levels are multiplied by the new correlated prices

to determine the 95th percentile worst case net exposure

if the company is employing a hedging strategy for a

commodity then it has no exposure for that particular

commodity since hedges are assumed to lock-in the

commodity at the baseline expected price

step 6 holistiC Commodity Risk pRoFile

CoRe AnAlysis Commodity exposures and price projections are combined with predefined estimates for

revenues and other fixed costssgA to illustrate both positive and negative impacts on the

earnings profile

outputs pro forma financial statement with predefined revenue and fixed costsgA reflecting the

impact of price variations and hedging on earnings

beneFits bull enables objective evaluation and comparison of a wide variety of risk

management strategies

bull promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at an

individual bu level

bull gives management a view of earnings impact from commodity prices at different levels

of probability

the process of determining net commodity exposure

ends with the creation of a holistic company-wide

risk profile ndash with the sensitivities in price projections

identified in step 1 used to explore the potential impact

on ebitdA debt covenants and other financial metrics

this impact is shown on the tab labeled lsquointerfacersquo in the

section called ldquoholistic commodity profile and impact

on earningsrdquo

Copyright copy 2012 oliver Wyman 9

ConClusion

large and sustained commodity price swings are quickly reshaping industries and the

businesses that exist within them in this environment commodity risk management must

be firmly integrated into the strategy of the company and structured under a commodity risk

management framework

executives need to recognize that their strategies may have to change to accommodate how

volatile commodity prices are redefining their competitive landscape and develop a process

to manage them across functions decisions around commodity risk management must be

based on a forward-looking view this process can include a series of tough questions about

the sustainability of business models such as

bull Will a perception of scarcity resulting from sustained volatile commodity prices impact

that commodityrsquos availability

bull Will changing commodity correlations invalidate diversification plays to hedge against

other commodities

bull Will consumers shift to cheaper private label alternatives or change their lifestyles to

avoid expenses introduced by higher commodity prices

the companies who are first to define their risk management metrics to include extreme

prices and anticipate how they can best benefit from them will have a significant

competitive advantage

Copyright copy 2012 oliver Wyman 10

Copyright copy 2012 oliver Wyman 11

For more information please contact

michael denton Partner global risk and trading oliver wyman

+16463648440 michaeldentonoliverwymancom

michael denton is a new york-based partner in oliver Wymanrsquos global Risk amp trading practice with specialized experience in energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market risk modelling portfolio dynamics and risk-based decision frameworks

alex wittenberg Partner global risk and trading oliver wyman

+16463648440 alexwittenbergoliverwymancom

Alex Wittenberg has over 20 years of cross-industry experience in risk management advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision making and financial performance designing risk governance for boards and management and developing corporate risk monitoring mitigation and transfer frameworks

brian t Kalish director Finance Practice lead

+13019616564 bkalishafponlineorg

brian t kalish is the director and Finance practice lead of the Association of Financial professionals he is responsible for the Finance practice which includes Corporate Finance Risk management Capital markets investor Relations Financial planning amp Analysis Accounting and Financial Reporting

oliveR WymAn And the AssoCiAtion FoR FinAnCiAl pRoFessionAls the FinAnCiAl insights seRies

this is part of series of papers by international management consulting firm oliver Wyman and the Association for Financial professionals that address critical issues for finance professionals oliver Wyman and AFp welcome your thoughts on the ideas presented in this paper if you would like to provide feedback or be alerted when we schedule events or other activities for corporate finance professionals please contact one of the individuals listed below

oliver Wyman is an international management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development

the Association for Financial professionals (AFp) headquartered outside Washington dC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFp is the daily resource for the finance profession

wwwoliverwymancom

Copyright copy oliver Wyman All rights reserved

AuthoRs

michael denton Partner global risk and trading oliver wyman

alex wittenberg Partner global risk and trading oliver wyman

brian t Kalish director Finance Practice lead

Gross Exposure graph support (stacked graph and waterfall)
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 5606 4238 1589 803 00
Grey 1244 1368 2649 786 803
Sugar 1244 186 261 341 456 1244
Natural gas 1368 210 278 375 505 1368
Electricity 2649 381 636 762 869 2649
Diesel 786 113 160 239 274 786
Aluminum 803 106 164 231 302 803
Commodities volume graph support
Q1 Q2 Q3 Q4
Sugar 12800 13400 14100 15000
Natural gas 960 1010 1060 1110
Electricity 100 100 100 100
Diesel 1600 1680 1760 1850
Aluminum 160 170 180 190
Net exposure graph support
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 3734 2596 1276 1162 00
Grey 1913 1138 1320 114 1162
Sugar 1913 207 404 530 771 1913
Natural gas 1138 168 250 348 372 1138
Electricity 1320 221 266 331 502 1320
Diesel 114 26 39 12 38 114
Aluminum 1162 151 223 340 448 1162
Financial statement graph support
Cost (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 463 686 840 1116 3105
Volume (lbs) 1280 1340 1410 1500 463 686 840 1116 3105 Unhedged
Price 036 051 060 074
Natural gas 718 829 956 1008 718 829 956 1008 3511
Volume 96 101 106 111 718 829 956 1008 3511 Unhedged
Price 75 82 90 91
Electricity 844 888 953 1125 844 888 953 1125 3810
Volume 10 10 10 10 844 888 953 1125 3810 Unhedged
Price 844 888 953 1125
Diesel 385 416 407 453 385 416 407 453 1660
Volume 160 168 176 185 385 416 407 453 1660 Unhedged
Price 24 25 23 24
Aluminum 451 543 678 805 451 543 678 805 2477
Volume 16 17 18 19 451 543 678 805 2476841735006 Unhedged
Price 282 319 377 424
Total 2861 3361 3834 4507 2861 3361 3834 4507 14564
2861 3361 3834 4507 14563772697504 Unhedged
Cost (5)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 179 142 141 119 179 142 141 119 581
Volume (lbs) 1280 1340 1410 1500 179 142 141 119 581 Unhedged
Price 014 011 010 008
Natural gas 347 283 251 241 347 283 251 241 1123
Volume 96 101 106 111 347 283 251 241 1123 Unhedged
Price 36 28 24 22
Electricity 332 223 176 138 332 223 176 138 868
Volume 10 10 10 10 332 223 176 138 868 Unhedged
Price 332 223 176 138
Diesel 275 264 244 231 275 264 244 231 1015
Volume 160 168 176 185 275 264 244 231 1015 Unhedged
Price 17 16 14 12
Aluminum 241 230 236 205 241 230 236 205 911
Volume 16 17 18 19 241 230 236 205 911127595797 Unhedged
Price 150 135 131 108
Total 1373 1142 1048 935 1373 1142 1048 935 4498
95ile 5ile
Sales 480 504 529 556 480 504 529 556
Fixed Costs 120 120 120 120 120 120 120 120
Variable Costs 286 336 383 451 137 114 105 93
General amp Admin Exp 90 90 90 90 90 90 90 90
EBITDA (16) (42) (64) (105) 133 180 214 252
Title To quickly check to make sure prices are still the same filter the prices on the tab proj 3 to go in order 1 to 100 and this should match
[Optional comments]
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
Earnings statement
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015
000
Cost of Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($lbs) 2560 2814 3102 3450 11926
Volume (lbs) 12800 13400 14100 15000
Price 020 021 022 023
000
Natural gas ($Kcf) - Henry Hub 5502 5788 6075 6362 23727
Volume 960 1010 1060 1110
Price 573 573 573 573
000
Electricity ($MWh) - NY ISO 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($gal) - NY Harbour 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
000
General amp Admin Exp 9000 9000 9000 9000 36000
000
EBITDA 6115 7608 9186 10802 33710
Impact of hedging
all figures in millions (except prices)
Futures Used
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
Net exposure (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 207 404 530 771 1913
Volume (lbs) 1280 1340 1410 1500
Price 036 051 060 074
00 00 00 00 00
Natural gas 718 829 956 1008 168 250 348 372 1138
Volume 96 101 106 111
Price 75 82 90 91
00 00 00 00 00
Electricity 844 888 953 1125 221 266 331 502 1320
Volume 10 10 10 10
Price 844 888 953 1125
00 00 00 00 00
Diesel 385 416 407 453 26 39 12 38 114
Volume 160 168 176 185
Price 24 25 23 24
00 00 00 00 00
Aluminum 451 543 678 805 151 223 340 448 1162
Volume 16 17 18 19
Price 282 319 377 424
Total 2861 3361 3834 4507 773 1182 1561 2131 5647
Correlated Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum Total Net Exposure
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Full-Year
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Volume 12800 13400 14100 15000 960 1010 1060 1110 100 100 100 100 1600 1680 1760 1850 160 170 180 190
Simulated
5 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 48715
95 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145722
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
Mean
Net Exposure Net Portfolio Exposure
32380 47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739 32380
33948 42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789 33948
39948 55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685 39948
43542 60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103 43542
44977 68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 44977
48911 50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956 48911
49824 97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168 49824
52614 9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173 52614
54247 89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834 54247
54467 29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518 54467
56081 37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959 56081
56950 66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358 56950
58740 44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783 58740
59233 63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890 59233
60239 11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751 60239
60316 3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213 60316
61664 56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573 61664
65269 4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321 65269
65273 92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204 65273
65764 69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759 65764
65876 93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008 65876
66078 87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886 66078
66116 74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632 66116
66562 28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686 66562
66781 5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205 66781
68244 27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291 68244
69290 94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559 69290
69701 32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209 69701
70796 10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158 70796
71222 62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991 71222
71261 45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691 71261
72702 91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228 72702
73734 20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550 73734
73801 52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806 73801
74108 88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414 74108
74317 1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024 74317
74660 22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160 74660
74808 31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368 74808
74998 82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171 74998
75350 8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146 75350
75850 85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158 75850
76270 2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409 76270
78037 30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375 78037
78226 23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103 78226
81022 96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662 81022
82068 99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748 82068
82643 35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858 82643
87060 14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447 87060
87614 83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068 87614
89614 46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928 89614
90297 100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267 90297
91492 49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762 91492
92285 79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150 92285
92517 80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079 92517
92520 58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466 92520
93297 38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511 93297
93492 84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782 93492
94010 72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702 94010
95355 25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982 95355
95981 51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310 95981
96590 70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430 96590
96659 59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987 96659
98984 39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335 98984
99473 64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881 99473
100438 18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643 100438
104925 26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805 104925
106683 71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274 106683
107046 48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657 107046
108929 75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922 108929
109789 17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544 109789
109940 98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393 109940
111963 61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121 111963
113140 41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558 113140
114475 15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927 114475
115420 76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479 115420
115470 12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839 115470
115543 43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938 115543
115599 16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233 115599
115628 95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056 115628
115695 13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336 115695
116747 78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058 116747
118794 33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808 118794
120798 67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960 120798
122134 7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729 122134
123516 73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263 123516
125606 90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639 125606
126928 54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417 126928
128846 77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264 128846
128878 6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513 128878
130807 34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239 130807
140398 19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517 140398
142414 81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603 142414
143917 53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292 143917
145005 65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126 145005
145638 36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145638
147330 40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883 147330
149955 86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437 149955
167385 24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467 167385
178469 57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836 178469
195830 21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430 195830
Risk management portfolio
Futures Description
Sugar Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Natural gas Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Electricity Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Diesel Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Aluminum Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Gross exposure calculations
Gross Exposure
all figures in millions (except prices) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 442 543 651 801 186 261 341 456 1244
Volume (lbs) 1280 1340 1410 1500
Price 035 041 046 053
00 00 00 00 00
Natural gas 760 857 982 1141 210 278 375 505 1368
Volume (MMBTU) 96 101 106 111
Price 792 849 927 1028
00 00 00 00 00
Electricity 1004 1259 1384 1492 381 636 762 869 2649
Volume (MWh) 10 10 10 10
Price 1004 1259 1384 1492
00 00 00 00 00
Diesel 473 537 634 689 113 160 239 274 786
Volume (gal) 160 168 176 185
Price 295 320 360 373
00 00 00 00 00
Aluminum 406 483 569 659 106 164 231 302 803
Volume (tons) 16 17 18 19
Price 2540 2843 3160 3469
Total 3085 3679 4220 4783 996 1500 1947 2407 6850
Sales Pricing and Volumes
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015 016
000
Cost of Commodity Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($) 2560 2814 3102 3450 11926
Volume (lb) 12800 13400 14100 15000
($lb) 020 021 022 023
000
Natural gas ($) 5502 5788 6075 6362 23727
Volume (MMBTU) 960 1010 1060 1110
Price ($MMBTU) 573 573 573 573
000
Electricity ($) 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($l) 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum ($) 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
Correlations
Correlated random shocks generated from correlation matrix
Correlations Sugar Natural gas Electricity Diesel Aluminum
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Sugar Q1 100 099 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q2 099 100 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q3 099 099 100 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q4 099 099 099 100 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Nat Gas Q1 065 065 065 065 100 099 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q2 065 065 065 065 099 100 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q3 065 065 065 065 099 099 100 099 094 094 094 094 086 086 086 086 069 069 069 069
Q4 065 065 065 065 099 099 099 100 094 094 094 094 086 086 086 086 069 069 069 069
Electricity Q1 055 055 055 055 094 094 094 094 100 099 099 099 082 082 082 082 071 071 071 071
Q2 055 055 055 055 094 094 094 094 099 100 099 099 082 082 082 082 071 071 071 071
Q3 055 055 055 055 094 094 094 094 099 099 100 099 082 082 082 082 071 071 071 071
Q4 055 055 055 055 094 094 094 094 099 099 099 100 082 082 082 082 071 071 071 071
Diesel Q1 047 047 047 047 086 086 086 086 082 082 082 082 100 099 099 099 066 066 066 066
Q2 047 047 047 047 086 086 086 086 082 082 082 082 099 100 099 099 066 066 066 066
Q3 047 047 047 047 086 086 086 086 082 082 082 082 099 099 100 099 066 066 066 066
Q4 047 047 047 047 086 086 086 086 082 082 082 082 099 099 099 100 066 066 066 066
Aluminum Q1 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 100 099 099 099
Q2 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 100 099 099
Q3 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 100 099
Q4 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 099 100
Column C C C C G G G G - 0 K K K - 0 O O O S S S S
Indirect C_Prices - 0 - 0
Correlated Sugar Natural gas Electricity Diesel Aluminum
N(01) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1 (108) (092) (082) (107) (047) (078) (057) (062) (015) (003) (008) (027) 041 052 041 022 052 051 036 039
2 031 (007) (006) (003) (015) (014) (012) (009) (033) (017) (021) (009) 005 005 026 (009) (087) (103) (079) (074)
3 (176) (167) (159) (155) (092) (079) (090) (098) (058) (061) (057) (069) (009) 018 007 002 (124) (101) (128) (121)
4 (134) (119) (112) (122) (046) (044) (043) (052) (066) (052) (037) (044) 016 009 (016) (003) (106) (116) (111) (094)
5 (075) (080) (049) (077) (063) (028) (057) (072) (045) (056) (051) (042) 011 010 021 032 (135) (127) (130) (123)
6 082 082 072 095 137 110 115 132 119 098 119 143 095 104 085 108 092 107 098 107
7 035 050 038 044 098 108 105 100 140 129 127 131 123 119 119 139 (024) (022) (013) (010)
8 (056) (044) (076) (066) (017) (033) (033) (045) (009) (007) 003 016 (091) (074) (102) (071) 078 068 080 058
9 (025) (015) (023) (027) (104) (127) (109) (105) (135) (150) (144) (166) (149) (148) (165) (151) (130) (126) (134) (131)
10 126 116 115 112 (120) (097) (107) (114) (054) (046) (052) (053) (107) (099) (125) (099) (117) (120) (125) (135)
11 (166) (182) (173) (174) (090) (083) (095) (089) (061) (070) (065) (060) (066) (048) (061) (084) 006 (013) (002) (006)
12 014 014 034 034 074 062 073 046 131 148 164 155 003 (003) 002 (035) 006 (003) 000 010
13 034 028 011 035 142 130 113 120 118 112 086 091 060 030 044 052 085 081 088 084
14 (048) (059) (063) (068) 033 026 020 050 021 017 019 023 001 (012) 009 005 089 097 075 099
15 079 091 090 091 083 082 073 081 096 086 093 081 107 112 115 096 047 038 030 024
16 058 071 034 059 116 117 122 103 064 084 084 092 081 080 068 108 093 092 082 070
17 (046) (041) (043) (048) 099 112 121 101 138 111 102 126 031 049 027 052 (019) (030) (033) (045)
18 030 052 025 024 060 078 068 059 073 093 069 070 031 033 026 030 (020) (037) (024) (026)
19 (034) (031) (025) (026) 146 114 124 151 166 173 175 161 121 116 108 097 222 227 201 212
20 (119) (138) (136) (114) (009) (009) 003 (012) (048) (021) (028) (040) 073 048 037 039 (028) (009) (003) (044)
21 092 081 085 085 236 228 257 241 229 231 230 239 259 264 269 263 098 082 075 097
22 047 052 031 042 (014) (030) (006) (016) (028) (027) (041) (031) (035) (040) (014) (031) (122) (127) (135) (134)
23 (043) (047) (046) (069) (021) (019) (014) (044) (005) 020 005 (006) (032) (025) (021) (012) 075 060 052 051
24 082 083 064 089 195 192 202 195 198 209 195 198 138 109 128 113 201 194 206 208
25 039 030 035 039 042 037 048 054 067 069 066 059 (050) (065) (051) (061) 032 038 040 033
26 130 157 113 138 057 052 036 048 076 085 063 076 (053) (058) (061) (052) 009 004 (012) 004
27 (063) (063) (067) (046) (041) (045) (053) (035) 012 015 004 (003) (140) (136) (152) (120) (116) (127) (101) (101)
28 (051) (067) (068) (058) (075) (063) (051) (068) (056) (050) (053) (068) (031) (025) (019) (053) (022) (005) (014) (018)
29 (165) (163) (176) (155) (115) (130) (143) (128) (065) (067) (081) (085) (128) (105) (126) (127) (058) (047) (031) (050)
30 (029) (031) (029) (032) (007) (016) (019) (003) 010 018 005 012 001 017 007 (007) (077) (089) (071) (081)
31 (040) (055) (058) (043) (004) (009) (010) (041) (007) (023) (027) (028) (065) (082) (086) (075) 078 068 065 089
32 123 098 102 105 (047) (059) (069) (079) (101) (106) (094) (097) (069) (074) (072) (089) (125) (131) (115) (122)
33 057 052 066 070 082 074 077 075 085 082 081 079 079 090 068 091 245 239 238 237
34 174 156 162 156 142 131 136 135 081 071 075 086 143 133 150 126 046 052 053 071
35 (121) (127) (111) (122) (004) (005) 018 (009) 051 059 046 032 (021) 003 (018) (008) 034 046 012 013
36 187 205 195 203 123 123 130 120 105 097 101 120 046 051 025 042 262 246 265 270
37 (019) (031) (029) (029) (106) (097) (106) (108) (103) (098) (084) (097) (090) (092) (107) (106) (199) (187) (190) (194)
38 080 080 081 069 018 036 (001) 014 (002) (022) (018) (017) 074 071 073 069 109 102 092 107
39 040 053 044 058 085 065 070 063 020 040 029 023 029 053 042 047 076 059 077 084
40 085 100 108 089 134 123 111 133 158 169 164 168 151 138 173 169 149 174 152 150
41 082 097 061 076 101 085 105 108 092 079 093 076 111 111 112 113 (045) (064) (044) (042)
42 (123) (134) (135) (147) (263) (271) (274) (254) (270) (276) (279) (268) (210) (223) (208) (227) (245) (244) (236) (255)
43 (016) (018) (018) (034) 100 092 094 115 122 134 110 112 115 109 102 111 027 024 035 026
44 (048) (052) (042) (035) (120) (122) (121) (119) (085) (098) (098) (095) (112) (115) (120) (132) (006) 005 001 (000)
45 069 067 059 054 (073) (073) (070) (067) (057) (073) (070) (080) (033) (026) (039) (057) (045) (040) (018) (017)
46 005 011 013 005 014 048 036 037 031 036 041 047 (043) (018) (052) (039) 030 037 050 024
47 (235) (256) (232) (222) (247) (231) (242) (221) (232) (219) (219) (235) (272) (277) (282) (278) (312) (301) (287) (275)
48 143 131 119 126 084 106 097 108 (004) 017 025 021 087 072 067 071 (004) 001 (000) (023)
49 (063) (045) (054) (035) 023 021 028 027 075 105 080 081 (048) (052) (049) (045) (006) 001 002 (004)
50 (055) (060) (066) (079) (129) (132) (138) (130) (154) (145) (127) (155) (157) (121) (133) (121) (205) (205) (217) (195)
51 039 028 044 031 052 056 043 052 030 025 046 023 035 034 022 042 079 079 067 081
52 (087) (075) (088) (067) (009) 002 (016) (002) 007 (011) (013) 000 (087) (077) (079) (081) (001) 016 (007) 004
53 (032) (024) (025) (035) 149 156 161 156 156 170 148 179 148 147 170 141 170 188 173 191
54 186 179 168 155 124 117 143 121 053 066 061 058 106 101 095 112 093 084 097 095
55 (135) (144) (137) (150) (190) (192) (201) (188) (170) (167) (161) (174) (185) (199) (201) (171) (296) (281) (273) (299)
56 (052) (051) (058) (040) (075) (083) (084) (087) (111) (124) (115) (109) (019) (009) 002 (019) (046) (047) (058) (039)
57 173 174 186 206 185 163 174 165 179 199 202 204 095 113 095 113 240 264 251 239
58 076 087 072 084 039 077 056 072 (005) 026 004 024 008 (004) (018) (002) (056) (058) (067) (061)
59 (072) (037) (056) (072) 059 073 063 057 048 051 060 051 074 102 100 080 033 037 022 034
60 (109) (093) (104) (106) (170) (175) (156) (156) (192) (185) (165) (178) (238) (234) (237) (243) (122) (127) (109) (150)
61 044 048 032 047 104 106 096 106 063 077 086 081 102 092 089 080 079 079 059 054
62 (105) (101) (086) (094) 009 (006) (009) (007) (027) (025) (023) (025) 026 015 030 041 (161) (157) (166) (184)
63 (095) (103) (105) (122) (077) (072) (095) (081) (123) (108) (120) (115) (077) (075) (081) (075) 020 018 028 018
64 (079) (070) (083) (086) 074 070 060 069 062 065 077 059 121 103 119 133 016 011 (005) 017
65 096 093 106 097 146 131 129 142 147 140 150 146 169 166 156 154 176 197 186 175
66 (049) (046) (037) (042) (084) (088) (081) (084) (110) (125) (112) (120) (149) (140) (151) (124) (042) (051) (064) (085)
67 061 045 052 064 096 126 115 098 107 100 105 103 157 142 150 155 009 034 031 029
68 (085) (114) (100) (117) (180) (194) (192) (178) (165) (186) (182) (184) (135) (123) (134) (140) (131) (134) (116) (157)
69 (084) (062) (057) (060) (071) (050) (052) (053) (104) (110) (109) (100) 028 002 031 008 (003) (012) (008) (004)
70 (002) 011 007 002 058 065 058 057 042 027 035 034 069 050 045 037 066 076 106 097
71 023 024 035 027 075 066 071 081 095 084 089 077 039 040 032 039 076 096 076 076
72 (005) (026) (008) (013) 061 048 062 036 027 026 034 017 121 143 116 129 (056) (025) (024) (015)
73 048 050 056 053 118 111 110 109 097 090 099 083 203 196 178 200 072 102 084 074
74 (119) (122) (110) (137) (040) (041) (045) (036) (033) (031) (018) (030) (071) (076) (086) (093) (044) (057) (050) (028)
75 114 095 103 110 098 087 111 092 026 041 026 023 109 106 108 120 027 029 017 023
76 057 048 022 022 085 080 076 090 086 072 090 077 167 169 163 169 098 084 086 103
77 190 171 152 155 112 132 124 105 111 085 105 108 040 053 065 059 046 074 069 075
78 077 059 048 077 080 079 090 080 111 120 103 119 089 077 070 073 059 064 042 045
79 118 100 116 107 005 (002) 006 011 010 031 016 013 (033) (034) (056) (059) 046 050 062 058
80 033 017 045 032 047 053 045 050 (010) 004 (012) (001) 066 093 082 077 045 034 044 048
81 182 197 190 205 085 101 107 099 114 122 103 125 081 073 071 071 197 192 209 220
82 076 081 062 078 (080) (086) (091) (084) (093) (085) (087) (086) (006) (014) (000) 009 082 069 074 061
83 156 153 147 166 006 (004) 020 003 (019) (018) (026) (025) (052) (052) (037) (048) (178) (171) (159) (160)
84 017 (010) (000) 003 048 058 066 040 042 051 037 048 020 052 037 030 006 006 (001) (000)
85 006 007 006 (000) 009 (020) (017) (013) 010 005 007 023 (045) (064) (063) (069) (131) (148) (138) (135)
86 078 080 076 077 162 150 131 145 144 154 144 151 281 273 262 269 091 085 107 097
87 (034) (019) (010) (011) (080) (060) (085) (074) (086) (087) (093) (097) (024) (034) (033) (036) (001) 001 010 017
88 (070) (046) (064) (058) (028) (040) (049) (046) (009) 005 (021) (021) 038 056 036 033 (045) (052) (050) (073)
89 (031) (037) (025) (013) (099) (097) (094) (098) (097) (107) (096) (084) (158) (167) (144) (163) (221) (230) (232) (238)
90 123 130 136 128 057 060 088 099 103 109 105 089 112 116 115 112 133 118 133 122
91 (146) (126) (133) (132) (037) (026) (029) (035) (010) (020) (038) (029) 000 (024) (020) (022) 059 068 077 069
92 (001) 007 (009) 018 (073) (053) (058) (058) (065) (078) (068) (070) (048) (052) (051) (052) (112) (143) (127) (123)
93 (226) (202) (225) (216) (056) (079) (073) (055) (020) (046) (045) (052) (011) (017) (014) (016) 027 021 039 037
94 (131) (113) (112) (115) (025) (019) (023) (024) (021) (013) (037) (047) (034) (005) (048) (023) (037) (042) (041) (042)
95 (087) (084) (091) (090) 066 068 041 072 116 108 119 121 152 144 136 136 152 180 159 168
96 (042) (035) (040) (038) (018) (001) (010) (018) 011 004 025 008 031 040 035 039 (011) (013) (012) (022)
97 (151) (157) (167) (162) (122) (147) (143) (134) (141) (132) (114) (134) (093) (074) (093) (093) (108) (119) (109) (132)
98 096 101 091 109 097 102 101 107 070 074 071 040 097 098 089 099 (062) (089) (089) (077)
99 033 028 029 021 (008) (017) (022) (009) (001) (010) 018 (002) 027 008 007 033 (038) (031) (030) (006)
100 (055) (051) (066) (067) 011 011 029 005 018 022 034 038 091 085 091 088 073 071 087 075
Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Simulated
5 011 009 007 006 407 327 289 267 3441 2649 2197 1553 165 147 131 120 1348 1197 1063 958
95 035 041 046 053 792 849 927 1028 10040 12591 13844 14919 295 320 360 373 2540 2843 3160 3469
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024
2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409
3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213
4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321
5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205
6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513
7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729
8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146
9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173
10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158
11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751
12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839
13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336
14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447
15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927
16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233
17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544
18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643
19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517
20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550
21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430
22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160
23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103
24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467
25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982
26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805
27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291
28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686
29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518
30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375
31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368
32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209
33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808
34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239
35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858
36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238
37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959
38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511
39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335
40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883
41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558
42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789
43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938
44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783
45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691
46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928
47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739
48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657
49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762
50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956
51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310
52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806
53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292
54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417
55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685
56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573
57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836
58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466
59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987
60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103
61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121
62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991
63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890
64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881
65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126
66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358
67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960
68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081
69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759
70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430
71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274
72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702
73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263
74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632
75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922
76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479
77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264
78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058
79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150
80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079
81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603
82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171
83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068
84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782
85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158
86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437
87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886
88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414
89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834
90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639
91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228
92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204
93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008
94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559
95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056
96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662
97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168
98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393
99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748
100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1
1
185947613236 209700016612 381289955056 113496909508 105940968217
261410846662 278202957602 636440926272 159777507841 164064511715
340758878594 374684278258 761658671012 238893166595 230844682326
456070302878 505106365162 869191059145 27405034829 302394276185
1 1 1
2 2 2
3 3 3
4 4 4
2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure
Sugar Sugar Sugar Sugar Sugar Sugar Sugar
Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas
Electricity Electricity Electricity Electricity Electricity Electricity Electricity
Diesel Diesel Diesel Diesel Diesel Diesel Diesel
Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum
1
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
USER INPUTS RISK PROFILE OUTPUTS
Date 1112 5 Holistic commodity profile and impact on earnings (baseline 95 confidence band 5 confidence band)
Price and Volatility Base 5th -ile 95th -ile
Q1 Q2 Q3 Q4 2012 Un hedged Hedged Impact Un hedged Hedged Impact
Price Volatility Price Volatlity Price Volatility Price Volatility of hedge of hedge
Sugar ($lbs) $ 020 70 $ 021 70 $ 022 70 $ 023 70 Sales 2069 2069 2069 - 0 2069 2069 - 0
Natural gas ($MMBTU) $ 573 48 $ 573 48 $ 573 48 $ 573 48 Cost of Goods Sold
Electricity ($MWh) $ 6227 69 $ 6227 69 $ 6227 69 $ 6227 69 Fixed Costs 480 480 480 - 0 480 480 - 0
Diesel ($gal) $ 224 37 $ 224 37 $ 224 37 $ 224 37 Sugar 119 58 58 - 0 311 311 - 0
Aluminum ($KTon) $ 1878 32 $ 1878 32 $ 1878 32 $ 1878 32 Natural gas 237 112 112 - 0 351 351 - 0
Electricity 249 87 87 - 0 381 381 - 0
Exposure risk level 95 Diesel 155 101 101 - 0 166 166 - 0
Aluminum 131 91 91 - 0 248 248 - 0
Commodity volumes (in millions) Variable Costs 892 450 450 - 0 1456 1456 - 0
Q1 Q2 Q3 Q4 Total COGS 1372 930 930 - 0 1936 1936 - 0
Sugar (lbs) 1280 1340 1410 1500 Gen amp Admin Exp 360 360 360 - 0 360 360 - 0
Natural gas (MMBTU) 96 101 106 111 EBITDA 337 779 779 - 0 (228) (228) - 0
Electricity (MWh) 10 10 10 10
Diesel (gal) 160 168 176 185
Aluminum (KTon) 16 17 18 19 4 Net commodity exposure 2012 by commodity (in millions $)
Other financial statement assumptions
Q1 Q2 Q3 Q4
Sales volume (in millions) 3200 3360 3528 3704
Sales price ($) 015 015 015 015
Fixed costs ($ millions) 120 120 120 120
GampA Exp ($ millions) 90 90 90 90
Hedge Selection
Futures
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
3 Gross commodity exposure 2012 (in millions $)
a) By quarter b) By commodity
All exposures reported as the difference between the 95th percentile highest exposure and the expected baseline gross commodity exposure
2 Estimated commodity volumes for 2012 by quarters (variable units in millions $)
1 Commodity price projections (baseline 95 confidence band 5 confidence band)
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
Read Me - File Overview
Page 6: Commodity Risk Management - Oliver Wyman

ExhIBIT 2 WhY ThE VOLATILITY

CAUSE DESCRIPTION

Erratic weather Catastrophic weather events have affected production (2009 drought in Australia affected global wheat prices)

Emerging markets Growth in emerging markets has increased demand for food energy and raw materials Global food output will have to rise 70 by 2050 to meet demand while global energy demand is predicted to increase by 36 between 2008-2035

Speculation Emergence of commodities as an investment class (development of commodities-based ETFs)

Infrastructure spending Deteriorating infrastructure in developed countries and a lack of infrastructure in emerging economies hampers the physical flow of commodities

Supply-country strategies Development of market-distorting trade policies (Russian wheat export ban followed crop shortfall in 2010)

Purchasing-country strategies

Countries developing more sophisticated buying capabilities (Korea opened a grain-trading office in Chicago in 2011)

Food and Agriculture Organization of the United Nations World Summit on Food Security November 2009

International Energy Agency World Energy Outlook 2010

This volatility could persist for years particularly given that governments appear willing to disrupt or intervene in markets including halting exports These collective forces have created pressure for corporations to develop strategies to mitigate this growing risk While companies in agri-processing oil refining and wholesale electricity generation have developed formal approaches to trading and risk management programs the majority of companies need to do moremdashmuch more

Given the likelihood of further volatility in commodity prices every company must adopt an analytic risk framework based on a clear understanding of its exposure This paper offers guidance on developing a plan for managing commodity risks that draws on the best practices of companies from around the world The approach is built around the three pillars of a ldquobest practicesrdquo program governance infrastructure and analytics The paper provides an in-depth review of the role of analytics and outlines a new systematic approach to managing commodity risk illustrated through case studies

ONE COMPANYrsquoS MOVES TO TAME ITS VULNERABILITY TO VOLATILITY

A leading industrial company failed to deliver its projected earnings due to a decline in the profitability of non-core energy activities To measure the companyrsquos total exposure to energy volatilitymdashand quantify the potential effect on future profits it needed to develop an integrated energy risk profile In other words the company needed the ability to aggregate the energy exposure of each business unit and evaluate the effectiveness of existing mitigation efforts before it could truly understand net exposure

The company analyzed commodity volatilities and correlations to produce a probabilistic analysis and derive ldquoEPS-at-riskrdquo estimates demonstrating the portion of earnings that were vulnerable to these price volatilities It then adopted a risk assessment tool capable of performing scenario analysis linked to alternative market states and specific events integrating this discipline by training both operational and financial staff in Treasury Procurement Planning and Operations This helped the company to gain insights into the aspects of its energy exposure that were most sensitive to price movements under different future states and thus were targets for mitigation efforts

Copyright copy 2011 Oliver Wyman 4

2 COMMODITY RISK MANAGEMENT FRAMEWORK

DEVELOPING A COMMODITY RISK GOVERNANCE PROGRAM

A crude oil producermarketer in Eastern Europe was recently seeking to enhance margins by creating regional physical trading capability First the Board required stakeholder alignment on a multi-dimensional risk appetite statement to guide risk and scale considerations High level loss limits were linked to the risk appetite and from these ldquodesk-levelrdquo limits were calculated By codifying the risk limits in policies the equity usage growth targets and working capital requirements were made explicit and served to support all later decisions on the business expansion plan

A structured commodity risk management framework is built around three pillars

Governance Infrastructure and Analytics Together these pillars support both short-

term tactical decisions and long-term strategic initiatives (see Exhibit 3)

GOVERNANCE

The first pillar of this framework is Governance in which an organization identifies the main

objectives of its commodity risk management program Companies first create a risk

appetite statement In this critical step an organization defines its risk tolerance and

aligns it with its broader performance objectives The risk appetite statement thus

establishes the basis of the organizationrsquos risk limits

ExhIBIT 3 COMMODITY RISK MANAGEMENT FRAMEWORK

SHORT-TERM TACTICAL DECISION MAKING

LONG-TERM STRATEGIC DECISION MAKING

GOVERNANCE

bull Risk appetite and corporate objectivesbull Risk limits bull Risk policybull Delegations of Authoritybull Decision frameworks

INFRASTRUCTURE

bull Reportingbull Accountingbull ITsystemsbull Organizational designbull Human capital

ANALYTICS

bull Price forecastingbull Commodity exposure modeling bull Stress and scenario testingbull Hedge optimization

Copyright copy 2011 Oliver Wyman 5

INFRASTRUCTURE

The second pillar Infrastructure comprises the organizational structure systems and

human capital that companies need to measure and manage risks Organizations need to

assess whether they have the systems to capture and monitor the necessary data flows The

organizational structure is important because the volatility in commodity markets requires

discipline and a tight alignment between managing cost- and revenue-related risks across

the enterprise historically these functions worked independently but today that results in

inefficiencies or even conflicting actions by different internal teams human capital is also

key In some cases a corporation may not have the experience or capabilities to manage

commodity risks effectively While a company can outsource risk management to financial

institutions or other market participants it pays a significant premium for that service and

potentially forfeits any upside potential In the process it gives its ldquopartnerrdquo proprietary

information they can use to trade for solely their own benefit

INCREASING EFFECTIVENESS ThROUGh IMPROVED INFRASTRUCTURE

The effects of steady growth were slowly compromising the risk management effectiveness of a major North American crude and distillates marketertrader Reporting timeliness was eroding error rates were climbing and managementrsquos confidence in risk control was decreasing prompting a full review of the organizational design and processes This revealed that the risk control function (middle office) was still performing much of the trade reconciliation that trade details were transmitted by email and that most reporting was done through spreadsheets

Several straightforward organizational shifts and systems enhancements eliminated a number of bottlenecks and enabled risk reporting down to the cargo level by desk and counterparty

ANALYTICS

The third pillar of a commodity risk management program is Analytics Organizations

cannot assess their financial exposure to commodity price swings without robust analytical

tools These analytics (or ldquomodelingrdquo) platforms support decision making at every level

mdash by helping managers model the future paths of commodity prices conduct stress- and

scenario-testing and evaluate and optimize risk-return profiles using a range of price-risk

management strategies however analytical capabilities vary dramatically from firm to firm

Companies should take a sequential approach to developing a robust analytics framework

that will support commodity risk management

Copyright copy 2011 Oliver Wyman 6

ThINK LIKE A TRADER

Trading is viewed as a necessary evil by some corporations given the connotations of excessive speculation and risk-taking with derivatives (in this case commodity futures contracts) A recent review of annual reports reveals that many companies disclosing commodity positions caution that these are strictly for hedging purposes and that the company ldquodoes not hold financial derivative positions for the purposes of tradingrdquo

Whether they realize it or not many companies are in fact speculating on commodity pricesmdashon both the cost and revenue sides Procurement officers are tasked with getting the best price on purchases for the company and its business units So the procurement staff commonly enters into forward fixed-price arrangements to guarantee both supply and pricing Just like traders they have thus made a bet on future prices and concluded price risk management with that view in mind

Companies need to accept that the days of a ldquoset it and forget itrdquo approach to risk management are over That approach was fine when volatility was low and prices increased gradually over time Today companies need to empower designated executives to think like traders ldquoIs this a good or bad price Should I buy more or run down inventoryrdquo Since most companies arenrsquot traders by nature they need to create a trading playbook that is integrated with their overall game plans

First a company must understand its commodity risk profile This helps the organization

assess its net exposure to commodity pricesmdashand their inevitable volatilitymdashacross

business and customer segments Detailed analysis also allows the organization to then

develop long-term strategies to mitigate commodity-related risks

A commodity risk profile provides a common understanding for senior management and

a fact-based foundation for evaluating the effectiveness of risk-mitigation actions With

this knowledge management can determine if the companyrsquos current commodity risk

exposure is within its risk tolerance and whether it has communicated these expectations

to stakeholders This analysis also helps the management team evaluate different risk

management strategies It promotes risk mitigation at the portfolio level which helps

reveal any risks lurking in individual business units or departments In short this analysis

will allow the company to optimize risk-return positioning

ThE NET ExPOSURE ChALLENGE

Determining true net exposure requires consideration of several factors gross commodity exposure existing risk mitigations foreign exchange flows demand sensitivity and the interactions among these This can be challenging for an enterprise with wide-spread operations or multiple product lines Typically each factorrsquos net impact at the corporate level is first assessed and any offsets across factors can then be accounted for

Copyright copy 2011 Oliver Wyman 7

To manage commodity price risks in ways that are consistent with broader corporate

objectives companies need a robust set of analytic tools to calculate the current exposure

Exhibit 4 offers a six-step analytical approach that companies can use to determine the

effect of commodity price risks and mitigation efforts on key financial metrics

The first step in the analytical framework requires management to build a forecast

of commodity prices using simulations or other techniques The company should

complement these forecasts with an analysis of alternative price outcomes based on ldquostress

eventsrdquo to understand fully how prices may evolve

In the second step the management team estimates the volume of future commodity

purchases across the enterprise In Step 3 this demand forecast is combined with

price projections to determine the firmrsquos gross commodity exposure In Step 4 the risk

management strategies already in place are identified Then in Step 5 these are applied

against the gross exposure to generate a ldquonet commodity exposurerdquo for the enterprise The

process ends with Step 6 the creation of a holistic company-wide risk profile ndash with the

sensitivities in price projections identified in Step 1 used to explore the potential impact on

EBITDA debt covenants and other financial metrics

Copyright copy 2011 Oliver Wyman 8

ExhIBIT 4 CREATING A hOLISTIC COMMODITY RISK PROFILE

bull Use analytic engines to simulate potential commodity price pathways (data driven)

bull Incorporate market context and paradigm shiftsscenario analysis ( judgment driven)

bull Integrates historical patterns market intelligence and fundamental analysis

bull Unifies disparate views of expected and highlow commodity price scenarios across organization

bull Incorporates interrelationships and correlations between commodities and currencies

STEP ANALYSIS BENEFITS

bull Centralizes commodity requirements across business units and geographies

bull Enables testing of alternative commodity purchase requirements using price elasticity analysis

bull Determine expected commodity purchase volume based on sales expectations

bull Gives context of expected commodity exposure compared to PampL and other risks

bull Accounts for ldquonatural hedgesrdquo in the portfolio

bull Shows shifts due to changes in business mix commodity price expectations

bull Calculate expected commodity exposure (eg price multiplied by volume)

bull Brings together and coordinates dierent functions of the organization (eg Strategy Procurement Treasury) and geographies

bull Sets understanding of risk management options for key commodity exposures

bull Define options for managing commodity price risk

bull Centralize risk management options undertaken across organization

bull Helps management assess the eectiveness of the risk management portfolio vs desired exposure

bull Provides understanding of how commodity price risk flows through the organization

bull Calculate exposure after incorporating current risk management portfolio

bull Enables objective evaluation and comparison of a range of risk management strategies

bull Promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at individual business unit or department level

bull Provides view of the earnings impact from commodity prices at dierent levels of probability

bull Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

COMMODITY PRICEPROJECTIONS

SALES PRICINGANDPURCHASE VOLUMES

GROSS EXPOSURE

RISK MANAGEMENTPORTFOLIO

NET EXPOSURE

HOLISTIC COMMODITYRISK PROFILE

Source Oliver Wyman

Copyright copy 2011 Oliver Wyman 9

IMPROVING COMPETIVENESS WITh A STRATEGIC RESPONSE TO COMMODITY VOLATILITY

A global food ingredient processor was losing sales to much smaller competitors solely because those firms were much more responsive to the highly volatile product price By analyzing the impacts of its forward sales process and market price uncertainty a new strategy was developed to migrate customers to shorter term contracts and to begin linking prices to market indices ndash which immediately improved the firmrsquos competitive positioning

STRATEGIES FOR MANAGING COMMODITY RISK

With a well-defined risk profile and an understanding of its ability to manage risk a

company can build a plan that matches its net exposure to commodities with its tolerance

for risk To manage commodity prices in the short term companies generally have three

tools at their disposal

bull Product pricingmdash identifying customer segments where the company has the ability to

raise prices or create pricing structures that mitigate risk

bull Procurement contract structuringmdashdeveloping innovative risk-sharing contracts with

suppliers

bull Financial hedgingmdashusing financial instruments for hedging to reduce overall

risk exposure

The effectiveness of these short-term strategies depends on the size of the organizationrsquos

exposure to a given commodity mdash and the commodity itself For example financial hedging

works best in commodity markets that are liquid (eg energy products such as crude oil and

natural gas or agricultural products such as wheat and corn) Meanwhile passing higher

commodity costs to customers through price increases is often ineffective in competitive

markets such as consumer products however when commodity cost increases are

significant and widespread pass-through pricing might be more viable Some companies

have taken creative approaches to raising prices For instance a number of consumer

products companies have reduced the volume of productmdashwhile keeping the same

package sizemdashto maintain margins in the face of higher commodity costs Other firms have

substituted cheaper ingredients to lower their net product costs

Over the long term volatility in commodity prices affects the behaviors of consumers

companies and their suppliers In response some companiesmdashand even countries--have

embedded commodity risk strategies into their long-term strategic plans

Copyright copy 2011 Oliver Wyman 10

CONCLUSION

Large and sustained commodity price swings are reshaping whole industries This means

that commodity risk management can no longer be considered the sole responsibility of

the procurement or finance staff With rising commodity prices affecting both the short-

term earnings and long-term strategies it is imperative that C-level executives develop a

deeper understanding of how to mitigate these risks Developing a structured commodity

risk management program built around the components outlined here is crucial Given

the new realities of higher prices and more volatility in commodities organizations must

integrate commodity risk management into their day-to-day operations They also must

build it into their long-term strategies to ensure the viability of the firm itself

ABOUT THE AUTHORS

MICHAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman

Michael J Denton PhD is a New York based Partner in Oliver Wymanrsquos Global Risk amp Trading practice with specialized experience in

energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market

risk modeling portfolio dynamics and risk-based decision frameworks Recent projects have focused on due diligence evaluations

competitive contracting and counterparty credit risk mitigation

ALEX WITTENBERG

Partner Global Risk amp Trading Oliver Wyman

Alex is the Managing Partner of Oliver Wymanrsquos Global Risk Center and has over 20 years of cross-industry experience in risk management

advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision-making and financial performance designing

risk governance for Boards and Management and developing corporate risk monitoring mitigation and transfer frameworks

Copyright copy 2011 Oliver Wyman 11

Copyright copy 2011 Oliver Wyman All rights reserved This report may not be reproduced or redistributed in whole or in part without the written permission of Oliver Wyman and Oliver Wyman accepts no liability whatsoever for the actions of third parties in this respect

The information and opinions in this report were prepared by Oliver Wyman

This report is not a substitute for tailored professional advice on how a specific financial institution should execute its strategy This report is not investment advice and should not be relied on for such advice or as a substitute for consultation with professional accountants tax legal or financial advisers Oliver Wyman has made every effort to use reliable up-to-date and comprehensive information and analysis but all information is provided without warranty of any kind express or implied Oliver Wyman disclaims any responsibility to update the information or conclusions in this report Oliver Wyman accepts no liability for any loss arising from any action taken or refrained from as a result of information contained in this report or any reports or sources of information referred to herein or for any consequential special or similar damages even if advised of the possibility of such damages

This report may not be sold without the written consent of Oliver Wyman

wwwoliverwymancom

Oliver Wyman is a leading global management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development Oliver Wymanrsquos Global Risk Center is dedicated to analyzing increasingly complex risks that are reshaping industries governments and societies Its mission is to assist decision makers in addressing risks by combining Oliver Wymanrsquos rigorous analytical approach to risk management with leading thinking from professional associations non-governmental organizations and academic institutions For further information please visit wwwoliverwymancomglobalriskcenter

The Association for Financial Professionals (AFP) headquartered outside Washington DC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFP is the daily resource for the finance profession

For more information please contact

MIChAEL J DENTON

Partner Global Risk amp Trading Oliver Wyman +16463648423 michaeldentonoliverwymancom

ALEx WITTENBERG

Partner Global Risk amp Trading Oliver Wyman +16463648440 alexwittenbergoliverwymancom

BRIAN T KALISh

Director Finance Practice Lead +13019616564 bkalishafponlineorg

Oliver Wyman
File Attachment
Commodity volatilitypdf

intRoduCtion

given the current and future trends in commodity prices and volatility every company must

better understand its true commodity exposure Companies can often be squeezed by

rising and volatile commodity input prices that cannot be passed along to customers in

their entirety A commodity risk management program can help not all organizations can

or should adopt the sophisticated mechanisms of a pure commodity business however

most organizations particularly those in the middle of the value chain can improve their

commodity risk analytics

exhibit 1 CompAnies ARe ChAllenged by Rising Commodity volAtility

Output prices

bull Competitive pressuresbull Limited ability to pass

through higher costs

COMPANY

bull Understand current forecasts (starting position and price curve)

bull Trace impact of price volatility to earnings

bull Align commodity risk management program strategy with corporate objectives

Commodity risk management program strategy instruments etc

Input prices

bull Increasing volatilitybull Increasing absolute pricebull Breakdown in correlations

the starting point is to understand the companyrsquos holistic commodity risk profile using

analytics and modeling tools A holistic commodity risk profile helps the organization assess

its individual and net exposure to commodity pricesmdashand the inevitable volatilitymdashacross

business and customer segments on a forward-looking basis the profile provides a common

understanding for senior management and a ldquofact-basedrdquo foundation for evaluating the

effectiveness of current risk-mitigation actions and alternative risk management strategies

With this knowledge management teams can determine if current commodity risk exposure

is within the companyrsquos risk tolerance and communicate these expectations to stakeholders

it also helps to promote risk mitigation at the portfolio level by identifying the most

important drivers of overall risk and ensuring the capture of any offsetting risks that may be

present in short this analysis will allow the company to optimize risk-return positioning

this In Practice Guide provides an overview of a stepwise approach and analytic processes

necessary to develop an organizationrsquos net commodity exposure

Copyright copy 2012 oliver Wyman 3

six steps to deteRmining Commodity exposuRe And impACts

exhibit 2 provides an overview of a six-step analytical approach to determine the impact of

commodity price risks on key financial metrics

exhibit 2 six steps to CReAte A holistiC Commodity Risk pRoFile

6 HOLISTIC COMMODITY RISK PROFILE

5 NET EXPOSURE

4 RISK MANAGEMENT

PORTFOLIO

3 GROSS EXPOSURE

2 SALES PRICING AND COMMODITY

PURCHASE VOLUMES

1 COMMODITY PRICE

PROJECTIONS

STEPS

ANALYSISUse analytic engines to simulate potential commodity price pathways (data driven)

Incorporate market context and paradigm shifts scenario analysis (judgement driven)

Calculate expected commodity exposure (ie price multiplied by volume)

Define options for managing commodity price risk

Centralize risk management options undertaken across organization

Calculate exposure after incorporating current risk management portfolio

Determine impact of commodity price projections and exposure on financial metrics (eg EBITDA cash flow debt covenants)

Determine expected commodity purchase volume based on sales expectations

Copyright copy 2012 oliver Wyman 4

step 1 Commodity pRiCe pRojeCtions

CoRe AnAlysis user-defined baseline commodity prices and volatilities for a predefined set of commodity

inputs are incorporated into a standard simulation process to generate a distribution of

potential future price paths for each commodity

outputs A distribution of terminal price values for each commodity and time horizon

beneFits bull integrates historical patterns market intelligence and fundamental analysis

bull unifies views of commodity price scenarios across the organization

bull incorporates interrelationships and correlations between commodities and other price

risk factors (eg currencies)

the first step in the analytical framework requires

management to build assumptions for commodity

prices in the future using simulation or other techniques

the management team should complement these

forecasts with an analysis of alternative price outcomes

based on ldquostress eventsrdquo to understand fully how prices

may evolve

A simple price simulation model is presented in the

attached workbook (accessed through the pushpin

icon) the inputs for these simulations are located on

the tab labeled ldquointerfacerdquo under the heading ldquoprice

and volatilityrdquo the user defines base case prices and

volatilities for each commodity and forward period

(ie 1st quarter 2nd quarter and so on) the simulated

terminal value prices or outcomes for each commodity

and time horizon are shown on the tab labeled ldquoCmdy

price projrdquo each of these values reflects possible future

outcomes for commodity prices based on three factors

mean price volatility and time

the outputs of the simulations for each commodity are

summarized on the ldquointerfacerdquo page in the ldquoCommodity

price projectionsrdquo section in each case 5th and 95th

percentile outcomes are shown along with the baseline

price scenario over the course of the time period to

provide a richer understanding of possible outcomes

versus a single static forecast not surprisingly the

uncertainty of price forecasts grows with the increasing

time horizon

Copyright copy 2012 oliver Wyman 5

ReadMe

OverviewThe workbook has been prepared as a supplement to the In Practice Guide Six Steps to Assess Commodity Risk Exposure prepared by Oliver Wyman for AFP members and available at wwwafponlineorg The purpose of this workbook is to provide a simple tool for illustration purposes only to better understand and quantify commodity risk impacts on earmings The example focuses on a corporation which utilizes key commodities including natural gas diesel fuel sugar electricity and aluminum The components of the workbook include a primary Interface worksheet as well as support worksheets which house the calculations used to generate outputs on the Interface sheet Note that all user inputs and outputs are exclusively contained in the Interface sheet - all other sheets are for reference only Cells in white in the User Inputs block represent input cells (prices volatilities and hedge designation) Any changes in outputs are generated by using the Calculate button The Interface worksheet receives inputs and provides outputs for a probabilistic earnings forecast model and illustrates the impacts of commodity prices and volatilities on the earnings profile In particular each of the outputs reflects one of the steps in the 6 step process Outputs include(1) Commodity price projections - User-defined baseline commodity prices and volatilities for a pre-defined set of commodity inputs are incorporated into a standard simulation process to generate a distribution of potential future price paths for each commodity(2) Estimated commodity volumes - A predefined set of commodity volumes reflecting commodity demand over time(3) Gross commodity exposure - User-defined baseline prices are combined with simulated price paths and volumes to generate commodity exposure - measured as the difference between the 95th percentile worst case outcome and the baseline expected outcome (4) Net commodity exposure - Gross commodity exposure is adjusted for any financial or physical hedges for each of the commodities Hedges are assumed to lock in the commodity at the baseline expected price Price correlations between commodities are taken into account(5) Holistic commodity risk profile - Commodity exposures are combined with predefined estimates for revenues and other fixed costsSGA to illustrate both positive and negative impacts on the earnings profileKey assumptionsKey assumptions in the model include the following - A GBM process is used to simulate all of the commodity price paths - Correlations are predefined and fixed resulting in a static set of shocks used in the simulation process- User defined volumes and revenues are static- User defined prices and volatiliities are used as baseline assumptions for each quarter

Interface

Interface

REF
1

Cmdy Price Proj

Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
124418764137
1367693617635
2648580611483
786217932233
803244438443
5605736599794
124418764137
4238042982159
1367693617635
1589462370676
2648580611483
803244438443
786217932233
0
803244438443

Correlations

5
95
Mean
Sugar
01107087317
03452715728
02
00854552129
04050827214
021
00695646039
04616729635
022
0061034605
05340468686
023

Sales Pricing Volumes

Sugar
Natural gas
Electricity
Diesel
Aluminum

Gross Exposure

REF
EPS-at-risk
1

Risk Mgmt Portfolio

REF
EBITDA
1

Simulation Net Exposure

Diesel
Electricity
Natural gas
Crude oil
Price change
EPS impact
Invisible
Grey
Sugar
Natural gas
Electricity
Diesel
Aluminum
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
2012 Gross Exposure
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Natural gas
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Electricity
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Diesel
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
Aluminum
1912572265773
1138117311849
1319658025171
11406289411
1162397994146
3734236225276
1912572265773
2596118913427
1138117311849
1276460888256
1319658025171
1162397994146
11406289411
0
1162397994146
5
95
Mean
Natural Gas
40688610903
79154680028
57310928297
32742030186
84855775584
57310928297
28887025303
92658501718
57310928297
26679947564
10281600624
57310928297

Net Exposure

5
95
Mean
Electricity
344141498094
1003995388957
622705433902
264877650618
1259146360173
622705433902
219723962528
1384358671012
6227
15528763279
1491896493046
622705433902

Fin Statement

5
95
Mean
Diesel
16508189205
29538139317
22444582473
14672697038
31955148416
22444582473
13074767002
36018057848
22444582473
11953891033
37258114813
22444582473

Price shock contuity

5
95
Mean
Aluminum
134769028369
254013105136
1878
119721652401
284279599425
187771063122
106347176349
316018108859
187771063122
95839609124
346925945325
187771063122

Graph support

Sugar
Natural gas
Electricity
Diesel
Aluminum
128
96
1
16
16
134
101
1
168
17
141
106
1
176
18
150
111
1
185
19
95ile
5ile
Earnings statement
-161353539101
1326569803377
611516334878
-421367106558
1798147036403
760774397443
-642177203637
2144399264928
918591594906
-1050874848209
2521161989213
1080153466317
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Correlations among commodity prices dictate the way in which commodity prices move together and are used to develop commodity price projections via simulation
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Commodity volumes may be determined by reviewing sales and demand planning forecasts Volumes may be aggregated when sourced from different business divisions or regions to arrive at a corporate-wide volume estimate
Simulation of commodity prices provides a richer understanding of possible price outcomes vs a single static forecast Simulations also support analysis of possible physical andor financial hedging strategies
Disclaimer The calculation methodology is intended to support an illustrative high-level example of risk exposure determination More robust and accurate methods should be used to determine forward price evolution and subsequent risk measurement and mitigation
Oliver Wyman
File Attachment
AFP-OW Commodity Risk Calculatorxls

step 2 sAles pRiCing And puRChAsing volumes

CoRe AnAlysis user-defined commodity volumes for the predefined set of commodities are coupled

with the distribution of price outcomes to support the determination of gross and net

exposures to commodities sales volumes and prices are incorporated into the pro forma

financial statement to understand earnings impacts from commodity exposures

outputs A predefined set of commodity volumes reflecting commodity demand over time

beneFits bull Centralizes commodity requirements across business units and geographies

bull enables testing of alternative commodity purchase requirements using price

elasticity analysis

in the second step the management team must

estimate the volume of future commodity purchases

across the enterprise the estimated volumes for inputs

are going to be directly linked to sales forecasts and

thus may be derived from product demand profiles

While the current model focuses on price uncertainty

it is important to recognize the presence of volume

uncertainty as well the static volumes are estimated

and then entered by the user on the tab labeled

ldquointerfacerdquo under the heading ldquoCommodity volumesrdquo

Assumptions for product sales volumes and prices are

also entered on the ldquointerfacerdquo tab for later use in the

holistic commodity risk profile assessment

step 3 gRoss exposuRe

CoRe AnAlysis user-defined baseline prices are combined with simulated price paths and volumes to

generate commodity exposure as measured by the difference between the 95th percentile

worst case outcome and the baseline expected outcome

outputs A firmrsquos gross commodity exposure given commodity price and volume

beneFits bull gives context of expected commodity exposure in comparison to pampl and other risks

bull shows changes over time due to either changes in business mix or changes in

commodity price expectations

in the third step the expected gross commodity

exposure is determined the 95th percentile worst case

commodity costs located on the tab labeled ldquogross

exposurerdquo are found by multiplying each commodity

volume by price gross exposure is determined by

summing the difference between the baseline cost and

the simulated 95th percentile worst case cost for each

individual commodity exposure Alternative percentiles

may be selected using the ldquoexposure risk levelrdquo input

value on the ldquointerfacerdquo tab

Copyright copy 2012 oliver Wyman 6

step 4 Risk mAnAgement poRtFolio

CoRe AnAlysis Risk management strategies currently in place are identified

outputs portfolio of existing risk management options (eg a set of commodity derivative hedges)

beneFits bull brings together and coordinates different functions of the organization (ie strategy

sales and marketing procurement treasury) and geographies

bull sets common understanding of risk management options for key commodity exposures

After determining gross commodity exposure the next

step in the process is to identify existing risk management

strategies the tab labeled ldquoRisk mgmt portfoliordquo has

a predefined list of risk management options available

which limit the management decision to financial

hedges that are assumed to lock-in the commodity at the

baseline expected price on the tab labeled ldquointerfacerdquo

under the heading ldquohedge selectionrdquo the user selects

ldquoyesrdquo or ldquonordquo for each commodity depending on whether

the firm plans to hedge that particular commodity it

is important to note that management of commodity

price risk is not limited only to financial hedging rather

it typically involves a combination of four broad levers

ndash each (or a combination thereof) may be effective as

illustrated in exhibit 3

besides recognizing current risk management

strategies this step also alerts management of other

potential risk mitigation options that may be available to

manage key commodities exposures

Copyright copy 2012 oliver Wyman 7

exhibit 3 Risk mAnAgement options

a Product Pricing (incl ldquoPass-throughrdquo)

b Procurement contracts

c Financial hedging

d Value chain integration long-term strategies

oVerView bull use market pricing

power to recover

change in input costs ndash

revenue management

bull balancesharetransfer

risk on purchases ndash

cost management

bull transfer risk to third

party through markets

structured deals

(Reinsurers also providing

capacity here)

bull up or downstream

integration eg

sourcing raw materials

retail trading

examPle strategies

bull employ cost pass-through

mechanisms (across

all or a portion of the

product portfolio)

bull Align price risk transfer

mechanisms with sales and

purchase contracts

bull include contract

mechanisms that mitigate

risk (eg price ceilings

floors price bands knock-

inknock-out clauses)

bull hedge price exposure

with swaps to set the price

or options or collars to

limit downside

bull Cross-hedge exposure

with contracts with

strong correlation to

underlying exposure

bull Acquire upstream company

to ensure supply and

diversify risk exposure

bull identify drivers that

contribute greatest

percentage to overall risk

Pros bull Raw materials costs fully

or partially passed through

to customers

bull stabilized margins and

earnings certainty

bull protection against price

moves and volatility

bull transition risk back

to supplier under

certain structures

bull protection against

feed stock price moves

and volatility

bull no impact on product

pricingsupplier contracts

bull holistic approach to cost

and margin management

bull diversification of

risk exposure

cons bull Customers may substitute

products or move to

lower priced competitor

brands in response to

product price

bull suppliers demand

premium (higher price)

for risk sharing

bull need to find

receptive suppliers

bull liquidity premium and

market non-availability

for many commodities

bull basis risk from

cross-hedging

bull Requires appropriate

capital and expertise

for joint ventures

or acquisitions

bull increased organizational

complexity

Copyright copy 2012 oliver Wyman 8

step 5 net exposuRe

CoRe AnAlysis predefined financial hedges and price correlations between commodities are applied to

gross commodity exposure to determine net commodity exposure for the enterprise

outputs A firmrsquos net commodity exposure after incorporating its current risk management portfolio

beneFits bull Accounts for natural hedges

bull provides management with an understanding of the effectiveness of the risk

management portfolio vs desired exposure

bull provides broad understanding of how commodity price risk flows through

the organization

While calculating net exposure management must also

take into account the correlation between commodity

prices price movements of one commodity may be related

to the price movement of other commodities thereby

increasing or decreasing exposure depending on the

relative strength of the relationship to account for this the

net exposure of the portfolio must be determined on the

tab labeled ldquosimulation net exposurerdquo the distribution

of terminal price values for each commodity and time

period (eg 1st quarter 2nd quarter etc) are multiplied

against volume for that same time period A distribution

of net exposures is generated and 5th and 95th percentile

outcomes for the portfolio series are selected

A companyrsquos net exposure to commodity price volatility is

shown on the tab labeled ldquonet exposurerdquo the predefined

volume levels are multiplied by the new correlated prices

to determine the 95th percentile worst case net exposure

if the company is employing a hedging strategy for a

commodity then it has no exposure for that particular

commodity since hedges are assumed to lock-in the

commodity at the baseline expected price

step 6 holistiC Commodity Risk pRoFile

CoRe AnAlysis Commodity exposures and price projections are combined with predefined estimates for

revenues and other fixed costssgA to illustrate both positive and negative impacts on the

earnings profile

outputs pro forma financial statement with predefined revenue and fixed costsgA reflecting the

impact of price variations and hedging on earnings

beneFits bull enables objective evaluation and comparison of a wide variety of risk

management strategies

bull promotes risk mitigation at a portfolio level to minimize sub-optimal risk mitigation at an

individual bu level

bull gives management a view of earnings impact from commodity prices at different levels

of probability

the process of determining net commodity exposure

ends with the creation of a holistic company-wide

risk profile ndash with the sensitivities in price projections

identified in step 1 used to explore the potential impact

on ebitdA debt covenants and other financial metrics

this impact is shown on the tab labeled lsquointerfacersquo in the

section called ldquoholistic commodity profile and impact

on earningsrdquo

Copyright copy 2012 oliver Wyman 9

ConClusion

large and sustained commodity price swings are quickly reshaping industries and the

businesses that exist within them in this environment commodity risk management must

be firmly integrated into the strategy of the company and structured under a commodity risk

management framework

executives need to recognize that their strategies may have to change to accommodate how

volatile commodity prices are redefining their competitive landscape and develop a process

to manage them across functions decisions around commodity risk management must be

based on a forward-looking view this process can include a series of tough questions about

the sustainability of business models such as

bull Will a perception of scarcity resulting from sustained volatile commodity prices impact

that commodityrsquos availability

bull Will changing commodity correlations invalidate diversification plays to hedge against

other commodities

bull Will consumers shift to cheaper private label alternatives or change their lifestyles to

avoid expenses introduced by higher commodity prices

the companies who are first to define their risk management metrics to include extreme

prices and anticipate how they can best benefit from them will have a significant

competitive advantage

Copyright copy 2012 oliver Wyman 10

Copyright copy 2012 oliver Wyman 11

For more information please contact

michael denton Partner global risk and trading oliver wyman

+16463648440 michaeldentonoliverwymancom

michael denton is a new york-based partner in oliver Wymanrsquos global Risk amp trading practice with specialized experience in energy agriculture and the commodities risk and trading sector As a practitioner and as a consultant he has worked extensively in market risk modelling portfolio dynamics and risk-based decision frameworks

alex wittenberg Partner global risk and trading oliver wyman

+16463648440 alexwittenbergoliverwymancom

Alex Wittenberg has over 20 years of cross-industry experience in risk management advisory and risk transfer solutions Alex specializes in integrating risk into strategic decision making and financial performance designing risk governance for boards and management and developing corporate risk monitoring mitigation and transfer frameworks

brian t Kalish director Finance Practice lead

+13019616564 bkalishafponlineorg

brian t kalish is the director and Finance practice lead of the Association of Financial professionals he is responsible for the Finance practice which includes Corporate Finance Risk management Capital markets investor Relations Financial planning amp Analysis Accounting and Financial Reporting

oliveR WymAn And the AssoCiAtion FoR FinAnCiAl pRoFessionAls the FinAnCiAl insights seRies

this is part of series of papers by international management consulting firm oliver Wyman and the Association for Financial professionals that address critical issues for finance professionals oliver Wyman and AFp welcome your thoughts on the ideas presented in this paper if you would like to provide feedback or be alerted when we schedule events or other activities for corporate finance professionals please contact one of the individuals listed below

oliver Wyman is an international management consulting firm that combines deep industry knowledge with specialized expertise in strategy operations risk management organizational transformation and leadership development

the Association for Financial professionals (AFp) headquartered outside Washington dC serves a network of more than 16000 members with news economic research and data treasury certification programs networking events financial analytical tools training and public policy representation to legislators and regulators AFp is the daily resource for the finance profession

wwwoliverwymancom

Copyright copy oliver Wyman All rights reserved

AuthoRs

michael denton Partner global risk and trading oliver wyman

alex wittenberg Partner global risk and trading oliver wyman

brian t Kalish director Finance Practice lead

Gross Exposure graph support (stacked graph and waterfall)
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 5606 4238 1589 803 00
Grey 1244 1368 2649 786 803
Sugar 1244 186 261 341 456 1244
Natural gas 1368 210 278 375 505 1368
Electricity 2649 381 636 762 869 2649
Diesel 786 113 160 239 274 786
Aluminum 803 106 164 231 302 803
Commodities volume graph support
Q1 Q2 Q3 Q4
Sugar 12800 13400 14100 15000
Natural gas 960 1010 1060 1110
Electricity 100 100 100 100
Diesel 1600 1680 1760 1850
Aluminum 160 170 180 190
Net exposure graph support
Graph data 2012 Gross Exposure Sugar Natural gas Electricity Diesel Aluminum Q1 Q2 Q3 Q4 Total
Invisible 3734 2596 1276 1162 00
Grey 1913 1138 1320 114 1162
Sugar 1913 207 404 530 771 1913
Natural gas 1138 168 250 348 372 1138
Electricity 1320 221 266 331 502 1320
Diesel 114 26 39 12 38 114
Aluminum 1162 151 223 340 448 1162
Financial statement graph support
Cost (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 463 686 840 1116 3105
Volume (lbs) 1280 1340 1410 1500 463 686 840 1116 3105 Unhedged
Price 036 051 060 074
Natural gas 718 829 956 1008 718 829 956 1008 3511
Volume 96 101 106 111 718 829 956 1008 3511 Unhedged
Price 75 82 90 91
Electricity 844 888 953 1125 844 888 953 1125 3810
Volume 10 10 10 10 844 888 953 1125 3810 Unhedged
Price 844 888 953 1125
Diesel 385 416 407 453 385 416 407 453 1660
Volume 160 168 176 185 385 416 407 453 1660 Unhedged
Price 24 25 23 24
Aluminum 451 543 678 805 451 543 678 805 2477
Volume 16 17 18 19 451 543 678 805 2476841735006 Unhedged
Price 282 319 377 424
Total 2861 3361 3834 4507 2861 3361 3834 4507 14564
2861 3361 3834 4507 14563772697504 Unhedged
Cost (5)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 179 142 141 119 179 142 141 119 581
Volume (lbs) 1280 1340 1410 1500 179 142 141 119 581 Unhedged
Price 014 011 010 008
Natural gas 347 283 251 241 347 283 251 241 1123
Volume 96 101 106 111 347 283 251 241 1123 Unhedged
Price 36 28 24 22
Electricity 332 223 176 138 332 223 176 138 868
Volume 10 10 10 10 332 223 176 138 868 Unhedged
Price 332 223 176 138
Diesel 275 264 244 231 275 264 244 231 1015
Volume 160 168 176 185 275 264 244 231 1015 Unhedged
Price 17 16 14 12
Aluminum 241 230 236 205 241 230 236 205 911
Volume 16 17 18 19 241 230 236 205 911127595797 Unhedged
Price 150 135 131 108
Total 1373 1142 1048 935 1373 1142 1048 935 4498
95ile 5ile
Sales 480 504 529 556 480 504 529 556
Fixed Costs 120 120 120 120 120 120 120 120
Variable Costs 286 336 383 451 137 114 105 93
General amp Admin Exp 90 90 90 90 90 90 90 90
EBITDA (16) (42) (64) (105) 133 180 214 252
Title To quickly check to make sure prices are still the same filter the prices on the tab proj 3 to go in order 1 to 100 and this should match
[Optional comments]
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
no no no no no no no no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes
Earnings statement
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015
000
Cost of Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($lbs) 2560 2814 3102 3450 11926
Volume (lbs) 12800 13400 14100 15000
Price 020 021 022 023
000
Natural gas ($Kcf) - Henry Hub 5502 5788 6075 6362 23727
Volume 960 1010 1060 1110
Price 573 573 573 573
000
Electricity ($MWh) - NY ISO 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($gal) - NY Harbour 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
000
General amp Admin Exp 9000 9000 9000 9000 36000
000
EBITDA 6115 7608 9186 10802 33710
Impact of hedging
all figures in millions (except prices)
Futures Used
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
Net exposure (95)
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 463 686 840 1116 207 404 530 771 1913
Volume (lbs) 1280 1340 1410 1500
Price 036 051 060 074
00 00 00 00 00
Natural gas 718 829 956 1008 168 250 348 372 1138
Volume 96 101 106 111
Price 75 82 90 91
00 00 00 00 00
Electricity 844 888 953 1125 221 266 331 502 1320
Volume 10 10 10 10
Price 844 888 953 1125
00 00 00 00 00
Diesel 385 416 407 453 26 39 12 38 114
Volume 160 168 176 185
Price 24 25 23 24
00 00 00 00 00
Aluminum 451 543 678 805 151 223 340 448 1162
Volume 16 17 18 19
Price 282 319 377 424
Total 2861 3361 3834 4507 773 1182 1561 2131 5647
Correlated Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum Total Net Exposure
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Full-Year
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Volume 12800 13400 14100 15000 960 1010 1060 1110 100 100 100 100 1600 1680 1760 1850 160 170 180 190
Simulated
5 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 48715
95 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145722
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
Mean
Net Exposure Net Portfolio Exposure
32380 47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739 32380
33948 42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789 33948
39948 55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685 39948
43542 60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103 43542
44977 68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081 44977
48911 50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956 48911
49824 97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168 49824
52614 9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173 52614
54247 89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834 54247
54467 29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518 54467
56081 37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959 56081
56950 66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358 56950
58740 44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783 58740
59233 63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890 59233
60239 11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751 60239
60316 3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213 60316
61664 56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573 61664
65269 4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321 65269
65273 92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204 65273
65764 69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759 65764
65876 93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008 65876
66078 87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886 66078
66116 74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632 66116
66562 28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686 66562
66781 5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205 66781
68244 27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291 68244
69290 94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559 69290
69701 32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209 69701
70796 10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158 70796
71222 62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991 71222
71261 45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691 71261
72702 91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228 72702
73734 20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550 73734
73801 52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806 73801
74108 88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414 74108
74317 1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024 74317
74660 22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160 74660
74808 31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368 74808
74998 82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171 74998
75350 8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146 75350
75850 85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158 75850
76270 2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409 76270
78037 30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375 78037
78226 23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103 78226
81022 96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662 81022
82068 99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748 82068
82643 35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858 82643
87060 14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447 87060
87614 83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068 87614
89614 46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928 89614
90297 100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267 90297
91492 49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762 91492
92285 79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150 92285
92517 80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079 92517
92520 58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466 92520
93297 38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511 93297
93492 84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782 93492
94010 72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702 94010
95355 25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982 95355
95981 51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310 95981
96590 70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430 96590
96659 59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987 96659
98984 39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335 98984
99473 64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881 99473
100438 18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643 100438
104925 26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805 104925
106683 71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274 106683
107046 48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657 107046
108929 75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922 108929
109789 17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544 109789
109940 98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393 109940
111963 61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121 111963
113140 41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558 113140
114475 15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927 114475
115420 76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479 115420
115470 12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839 115470
115543 43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938 115543
115599 16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233 115599
115628 95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056 115628
115695 13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336 115695
116747 78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058 116747
118794 33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808 118794
120798 67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960 120798
122134 7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729 122134
123516 73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263 123516
125606 90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639 125606
126928 54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417 126928
128846 77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264 128846
128878 6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513 128878
130807 34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239 130807
140398 19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517 140398
142414 81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603 142414
143917 53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292 143917
145005 65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126 145005
145638 36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238 145638
147330 40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883 147330
149955 86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437 149955
167385 24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467 167385
178469 57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836 178469
195830 21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430 195830
Risk management portfolio
Futures Description
Sugar Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Natural gas Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Electricity Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Diesel Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Aluminum Futures or OTC contracts may be used to lock in or fix commodity prices for defined periods of time
Gross exposure calculations
Gross Exposure
all figures in millions (except prices) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Total
Simulated commodity costs
Sugar 442 543 651 801 186 261 341 456 1244
Volume (lbs) 1280 1340 1410 1500
Price 035 041 046 053
00 00 00 00 00
Natural gas 760 857 982 1141 210 278 375 505 1368
Volume (MMBTU) 96 101 106 111
Price 792 849 927 1028
00 00 00 00 00
Electricity 1004 1259 1384 1492 381 636 762 869 2649
Volume (MWh) 10 10 10 10
Price 1004 1259 1384 1492
00 00 00 00 00
Diesel 473 537 634 689 113 160 239 274 786
Volume (gal) 160 168 176 185
Price 295 320 360 373
00 00 00 00 00
Aluminum 406 483 569 659 106 164 231 302 803
Volume (tons) 16 17 18 19
Price 2540 2843 3160 3469
Total 3085 3679 4220 4783 996 1500 1947 2407 6850
Sales Pricing and Volumes
all figures in millions (except prices) Q1 Q2 Q3 Q4 2012
Sales 48000 50400 52920 55560 206880
Volume 320000 336000 352800 370400 1379200
Price ($) 015 015 015 015 016
000
Cost of Commodity Goods Sold 32885 33792 34734 35758 137170
Fixed Costs 12000 12000 12000 12000 48000
Sugar ($) 2560 2814 3102 3450 11926
Volume (lb) 12800 13400 14100 15000
($lb) 020 021 022 023
000
Natural gas ($) 5502 5788 6075 6362 23727
Volume (MMBTU) 960 1010 1060 1110
Price ($MMBTU) 573 573 573 573
000
Electricity ($) 6227 6227 6227 6227 24908
Volume 100 100 100 100
Price 6227 6227 6227 6227
000
Diesel ($l) 3591 3771 3950 4152 15464
Volume 1600 1680 1760 1850
Price 224 224 224 224
000
Aluminum ($) 3005 3192 3380 3568 13144
Volume 160 170 180 190
Price 1878 1878 1878 1878
Variable Costs 20885 21792 22734 23758 89170
Correlations
Correlated random shocks generated from correlation matrix
Correlations Sugar Natural gas Electricity Diesel Aluminum
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Sugar Q1 100 099 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q2 099 100 099 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q3 099 099 100 099 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Q4 099 099 099 100 065 065 065 065 055 055 055 055 047 047 047 047 043 043 043 043
Nat Gas Q1 065 065 065 065 100 099 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q2 065 065 065 065 099 100 099 099 094 094 094 094 086 086 086 086 069 069 069 069
Q3 065 065 065 065 099 099 100 099 094 094 094 094 086 086 086 086 069 069 069 069
Q4 065 065 065 065 099 099 099 100 094 094 094 094 086 086 086 086 069 069 069 069
Electricity Q1 055 055 055 055 094 094 094 094 100 099 099 099 082 082 082 082 071 071 071 071
Q2 055 055 055 055 094 094 094 094 099 100 099 099 082 082 082 082 071 071 071 071
Q3 055 055 055 055 094 094 094 094 099 099 100 099 082 082 082 082 071 071 071 071
Q4 055 055 055 055 094 094 094 094 099 099 099 100 082 082 082 082 071 071 071 071
Diesel Q1 047 047 047 047 086 086 086 086 082 082 082 082 100 099 099 099 066 066 066 066
Q2 047 047 047 047 086 086 086 086 082 082 082 082 099 100 099 099 066 066 066 066
Q3 047 047 047 047 086 086 086 086 082 082 082 082 099 099 100 099 066 066 066 066
Q4 047 047 047 047 086 086 086 086 082 082 082 082 099 099 099 100 066 066 066 066
Aluminum Q1 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 100 099 099 099
Q2 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 100 099 099
Q3 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 100 099
Q4 043 043 043 043 069 069 069 069 071 071 071 071 066 066 066 066 099 099 099 100
Column C C C C G G G G - 0 K K K - 0 O O O S S S S
Indirect C_Prices - 0 - 0
Correlated Sugar Natural gas Electricity Diesel Aluminum
N(01) Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1 (108) (092) (082) (107) (047) (078) (057) (062) (015) (003) (008) (027) 041 052 041 022 052 051 036 039
2 031 (007) (006) (003) (015) (014) (012) (009) (033) (017) (021) (009) 005 005 026 (009) (087) (103) (079) (074)
3 (176) (167) (159) (155) (092) (079) (090) (098) (058) (061) (057) (069) (009) 018 007 002 (124) (101) (128) (121)
4 (134) (119) (112) (122) (046) (044) (043) (052) (066) (052) (037) (044) 016 009 (016) (003) (106) (116) (111) (094)
5 (075) (080) (049) (077) (063) (028) (057) (072) (045) (056) (051) (042) 011 010 021 032 (135) (127) (130) (123)
6 082 082 072 095 137 110 115 132 119 098 119 143 095 104 085 108 092 107 098 107
7 035 050 038 044 098 108 105 100 140 129 127 131 123 119 119 139 (024) (022) (013) (010)
8 (056) (044) (076) (066) (017) (033) (033) (045) (009) (007) 003 016 (091) (074) (102) (071) 078 068 080 058
9 (025) (015) (023) (027) (104) (127) (109) (105) (135) (150) (144) (166) (149) (148) (165) (151) (130) (126) (134) (131)
10 126 116 115 112 (120) (097) (107) (114) (054) (046) (052) (053) (107) (099) (125) (099) (117) (120) (125) (135)
11 (166) (182) (173) (174) (090) (083) (095) (089) (061) (070) (065) (060) (066) (048) (061) (084) 006 (013) (002) (006)
12 014 014 034 034 074 062 073 046 131 148 164 155 003 (003) 002 (035) 006 (003) 000 010
13 034 028 011 035 142 130 113 120 118 112 086 091 060 030 044 052 085 081 088 084
14 (048) (059) (063) (068) 033 026 020 050 021 017 019 023 001 (012) 009 005 089 097 075 099
15 079 091 090 091 083 082 073 081 096 086 093 081 107 112 115 096 047 038 030 024
16 058 071 034 059 116 117 122 103 064 084 084 092 081 080 068 108 093 092 082 070
17 (046) (041) (043) (048) 099 112 121 101 138 111 102 126 031 049 027 052 (019) (030) (033) (045)
18 030 052 025 024 060 078 068 059 073 093 069 070 031 033 026 030 (020) (037) (024) (026)
19 (034) (031) (025) (026) 146 114 124 151 166 173 175 161 121 116 108 097 222 227 201 212
20 (119) (138) (136) (114) (009) (009) 003 (012) (048) (021) (028) (040) 073 048 037 039 (028) (009) (003) (044)
21 092 081 085 085 236 228 257 241 229 231 230 239 259 264 269 263 098 082 075 097
22 047 052 031 042 (014) (030) (006) (016) (028) (027) (041) (031) (035) (040) (014) (031) (122) (127) (135) (134)
23 (043) (047) (046) (069) (021) (019) (014) (044) (005) 020 005 (006) (032) (025) (021) (012) 075 060 052 051
24 082 083 064 089 195 192 202 195 198 209 195 198 138 109 128 113 201 194 206 208
25 039 030 035 039 042 037 048 054 067 069 066 059 (050) (065) (051) (061) 032 038 040 033
26 130 157 113 138 057 052 036 048 076 085 063 076 (053) (058) (061) (052) 009 004 (012) 004
27 (063) (063) (067) (046) (041) (045) (053) (035) 012 015 004 (003) (140) (136) (152) (120) (116) (127) (101) (101)
28 (051) (067) (068) (058) (075) (063) (051) (068) (056) (050) (053) (068) (031) (025) (019) (053) (022) (005) (014) (018)
29 (165) (163) (176) (155) (115) (130) (143) (128) (065) (067) (081) (085) (128) (105) (126) (127) (058) (047) (031) (050)
30 (029) (031) (029) (032) (007) (016) (019) (003) 010 018 005 012 001 017 007 (007) (077) (089) (071) (081)
31 (040) (055) (058) (043) (004) (009) (010) (041) (007) (023) (027) (028) (065) (082) (086) (075) 078 068 065 089
32 123 098 102 105 (047) (059) (069) (079) (101) (106) (094) (097) (069) (074) (072) (089) (125) (131) (115) (122)
33 057 052 066 070 082 074 077 075 085 082 081 079 079 090 068 091 245 239 238 237
34 174 156 162 156 142 131 136 135 081 071 075 086 143 133 150 126 046 052 053 071
35 (121) (127) (111) (122) (004) (005) 018 (009) 051 059 046 032 (021) 003 (018) (008) 034 046 012 013
36 187 205 195 203 123 123 130 120 105 097 101 120 046 051 025 042 262 246 265 270
37 (019) (031) (029) (029) (106) (097) (106) (108) (103) (098) (084) (097) (090) (092) (107) (106) (199) (187) (190) (194)
38 080 080 081 069 018 036 (001) 014 (002) (022) (018) (017) 074 071 073 069 109 102 092 107
39 040 053 044 058 085 065 070 063 020 040 029 023 029 053 042 047 076 059 077 084
40 085 100 108 089 134 123 111 133 158 169 164 168 151 138 173 169 149 174 152 150
41 082 097 061 076 101 085 105 108 092 079 093 076 111 111 112 113 (045) (064) (044) (042)
42 (123) (134) (135) (147) (263) (271) (274) (254) (270) (276) (279) (268) (210) (223) (208) (227) (245) (244) (236) (255)
43 (016) (018) (018) (034) 100 092 094 115 122 134 110 112 115 109 102 111 027 024 035 026
44 (048) (052) (042) (035) (120) (122) (121) (119) (085) (098) (098) (095) (112) (115) (120) (132) (006) 005 001 (000)
45 069 067 059 054 (073) (073) (070) (067) (057) (073) (070) (080) (033) (026) (039) (057) (045) (040) (018) (017)
46 005 011 013 005 014 048 036 037 031 036 041 047 (043) (018) (052) (039) 030 037 050 024
47 (235) (256) (232) (222) (247) (231) (242) (221) (232) (219) (219) (235) (272) (277) (282) (278) (312) (301) (287) (275)
48 143 131 119 126 084 106 097 108 (004) 017 025 021 087 072 067 071 (004) 001 (000) (023)
49 (063) (045) (054) (035) 023 021 028 027 075 105 080 081 (048) (052) (049) (045) (006) 001 002 (004)
50 (055) (060) (066) (079) (129) (132) (138) (130) (154) (145) (127) (155) (157) (121) (133) (121) (205) (205) (217) (195)
51 039 028 044 031 052 056 043 052 030 025 046 023 035 034 022 042 079 079 067 081
52 (087) (075) (088) (067) (009) 002 (016) (002) 007 (011) (013) 000 (087) (077) (079) (081) (001) 016 (007) 004
53 (032) (024) (025) (035) 149 156 161 156 156 170 148 179 148 147 170 141 170 188 173 191
54 186 179 168 155 124 117 143 121 053 066 061 058 106 101 095 112 093 084 097 095
55 (135) (144) (137) (150) (190) (192) (201) (188) (170) (167) (161) (174) (185) (199) (201) (171) (296) (281) (273) (299)
56 (052) (051) (058) (040) (075) (083) (084) (087) (111) (124) (115) (109) (019) (009) 002 (019) (046) (047) (058) (039)
57 173 174 186 206 185 163 174 165 179 199 202 204 095 113 095 113 240 264 251 239
58 076 087 072 084 039 077 056 072 (005) 026 004 024 008 (004) (018) (002) (056) (058) (067) (061)
59 (072) (037) (056) (072) 059 073 063 057 048 051 060 051 074 102 100 080 033 037 022 034
60 (109) (093) (104) (106) (170) (175) (156) (156) (192) (185) (165) (178) (238) (234) (237) (243) (122) (127) (109) (150)
61 044 048 032 047 104 106 096 106 063 077 086 081 102 092 089 080 079 079 059 054
62 (105) (101) (086) (094) 009 (006) (009) (007) (027) (025) (023) (025) 026 015 030 041 (161) (157) (166) (184)
63 (095) (103) (105) (122) (077) (072) (095) (081) (123) (108) (120) (115) (077) (075) (081) (075) 020 018 028 018
64 (079) (070) (083) (086) 074 070 060 069 062 065 077 059 121 103 119 133 016 011 (005) 017
65 096 093 106 097 146 131 129 142 147 140 150 146 169 166 156 154 176 197 186 175
66 (049) (046) (037) (042) (084) (088) (081) (084) (110) (125) (112) (120) (149) (140) (151) (124) (042) (051) (064) (085)
67 061 045 052 064 096 126 115 098 107 100 105 103 157 142 150 155 009 034 031 029
68 (085) (114) (100) (117) (180) (194) (192) (178) (165) (186) (182) (184) (135) (123) (134) (140) (131) (134) (116) (157)
69 (084) (062) (057) (060) (071) (050) (052) (053) (104) (110) (109) (100) 028 002 031 008 (003) (012) (008) (004)
70 (002) 011 007 002 058 065 058 057 042 027 035 034 069 050 045 037 066 076 106 097
71 023 024 035 027 075 066 071 081 095 084 089 077 039 040 032 039 076 096 076 076
72 (005) (026) (008) (013) 061 048 062 036 027 026 034 017 121 143 116 129 (056) (025) (024) (015)
73 048 050 056 053 118 111 110 109 097 090 099 083 203 196 178 200 072 102 084 074
74 (119) (122) (110) (137) (040) (041) (045) (036) (033) (031) (018) (030) (071) (076) (086) (093) (044) (057) (050) (028)
75 114 095 103 110 098 087 111 092 026 041 026 023 109 106 108 120 027 029 017 023
76 057 048 022 022 085 080 076 090 086 072 090 077 167 169 163 169 098 084 086 103
77 190 171 152 155 112 132 124 105 111 085 105 108 040 053 065 059 046 074 069 075
78 077 059 048 077 080 079 090 080 111 120 103 119 089 077 070 073 059 064 042 045
79 118 100 116 107 005 (002) 006 011 010 031 016 013 (033) (034) (056) (059) 046 050 062 058
80 033 017 045 032 047 053 045 050 (010) 004 (012) (001) 066 093 082 077 045 034 044 048
81 182 197 190 205 085 101 107 099 114 122 103 125 081 073 071 071 197 192 209 220
82 076 081 062 078 (080) (086) (091) (084) (093) (085) (087) (086) (006) (014) (000) 009 082 069 074 061
83 156 153 147 166 006 (004) 020 003 (019) (018) (026) (025) (052) (052) (037) (048) (178) (171) (159) (160)
84 017 (010) (000) 003 048 058 066 040 042 051 037 048 020 052 037 030 006 006 (001) (000)
85 006 007 006 (000) 009 (020) (017) (013) 010 005 007 023 (045) (064) (063) (069) (131) (148) (138) (135)
86 078 080 076 077 162 150 131 145 144 154 144 151 281 273 262 269 091 085 107 097
87 (034) (019) (010) (011) (080) (060) (085) (074) (086) (087) (093) (097) (024) (034) (033) (036) (001) 001 010 017
88 (070) (046) (064) (058) (028) (040) (049) (046) (009) 005 (021) (021) 038 056 036 033 (045) (052) (050) (073)
89 (031) (037) (025) (013) (099) (097) (094) (098) (097) (107) (096) (084) (158) (167) (144) (163) (221) (230) (232) (238)
90 123 130 136 128 057 060 088 099 103 109 105 089 112 116 115 112 133 118 133 122
91 (146) (126) (133) (132) (037) (026) (029) (035) (010) (020) (038) (029) 000 (024) (020) (022) 059 068 077 069
92 (001) 007 (009) 018 (073) (053) (058) (058) (065) (078) (068) (070) (048) (052) (051) (052) (112) (143) (127) (123)
93 (226) (202) (225) (216) (056) (079) (073) (055) (020) (046) (045) (052) (011) (017) (014) (016) 027 021 039 037
94 (131) (113) (112) (115) (025) (019) (023) (024) (021) (013) (037) (047) (034) (005) (048) (023) (037) (042) (041) (042)
95 (087) (084) (091) (090) 066 068 041 072 116 108 119 121 152 144 136 136 152 180 159 168
96 (042) (035) (040) (038) (018) (001) (010) (018) 011 004 025 008 031 040 035 039 (011) (013) (012) (022)
97 (151) (157) (167) (162) (122) (147) (143) (134) (141) (132) (114) (134) (093) (074) (093) (093) (108) (119) (109) (132)
98 096 101 091 109 097 102 101 107 070 074 071 040 097 098 089 099 (062) (089) (089) (077)
99 033 028 029 021 (008) (017) (022) (009) (001) (010) 018 (002) 027 008 007 033 (038) (031) (030) (006)
100 (055) (051) (066) (067) 011 011 029 005 018 022 034 038 091 085 091 088 073 071 087 075
Price Simulations
Commodity prices are simulated through geometric brownian motion and utilize correlated random shocks based on an input correlation matrix
Sugar Natural gas Electricity Diesel Aluminum
Period Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Mean 020 021 022 023 573 573 573 573 6227 6227 6227 6227 224 224 224 224 1878 1878 1878 1878
Volatility 070 070 070 070 048 048 048 048 069 069 069 069 037 037 037 037 032 032 032 032
Time 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100 025 050 075 100
Simulated
5 011 009 007 006 407 327 289 267 3441 2649 2197 1553 165 147 131 120 1348 1197 1063 958
95 035 041 046 053 792 849 927 1028 10040 12591 13844 14919 295 320 360 373 2540 2843 3160 3469
Std Dev 007 010 012 016 135 186 238 270 2100 3074 3713 4521 044 062 077 091 339 493 596 707
1 013 012 011 009 497 415 414 379 5566 5460 4956 4077 238 249 243 227 2014 2054 1994 2024
2 021 018 018 018 538 515 500 489 5231 5090 4596 4623 223 220 231 203 1613 1450 1451 1409
3 010 008 007 006 446 413 362 318 4810 4104 3705 3050 217 227 218 212 1520 1456 1268 1213
4 012 010 009 008 499 466 440 399 4679 4289 4175 3630 227 222 202 207 1564 1407 1328 1321
5 014 012 014 010 479 493 416 361 5031 4209 3839 3683 225 222 228 236 1494 1374 1259 1205
6 025 028 028 035 774 786 849 960 8847 8937 10590 13150 263 285 280 313 2148 2332 2371 2513
7 021 024 023 025 704 782 813 827 9526 10396 11149 12151 277 296 312 350 1785 1741 1742 1729
8 015 015 012 011 534 484 458 411 5691 5348 5296 5469 186 179 154 161 2101 2134 2257 2146
9 017 017 016 015 434 352 335 309 3687 2659 2208 1557 167 147 126 120 1507 1377 1248 1173
10 029 033 037 040 417 389 337 296 4868 4408 3809 3401 181 167 143 145 1538 1394 1279 1158
11 011 008 006 005 449 408 354 334 4758 3935 3524 3236 195 191 176 153 1871 1777 1795 1751
12 020 020 022 023 665 668 712 637 9215 11363 13842 14327 222 215 215 184 1873 1818 1809 1839
13 021 021 020 023 783 842 842 907 8811 9563 8686 9220 246 234 246 254 2124 2197 2307 2336
14 016 014 012 011 603 590 570 649 6301 6010 5819 5771 221 210 220 213 2139 2281 2226 2447
15 025 029 032 034 680 714 712 755 8168 8424 9094 8582 269 290 309 299 1998 1996 1966 1927
16 023 026 023 027 735 805 871 836 7317 8319 8599 9260 256 267 265 313 2153 2255 2268 2233
17 016 015 014 013 706 792 871 828 9457 9496 9557 11733 234 246 232 254 1799 1710 1649 1544
18 021 024 021 021 643 705 699 679 7545 8706 7888 7960 234 236 232 234 1795 1682 1688 1643
19 017 016 016 015 791 795 881 1053 10420 12831 14842 14880 276 294 302 301 2645 3061 3158 3517
20 012 009 008 008 545 525 532 483 4964 4978 4418 3734 252 246 240 242 1773 1793 1792 1550
21 026 028 031 033 980 1173 1531 1623 12934 17045 20568 25587 356 432 504 555 2168 2201 2225 2430
22 022 024 022 024 539 489 513 474 5323 4839 4078 3974 207 195 204 187 1525 1374 1241 1160
23 016 015 014 011 529 506 495 414 5761 6103 5365 4695 208 203 199 201 2090 2095 2089 2103
24 025 028 027 033 889 1039 1216 1304 11617 15334 16748 19296 285 288 321 318 2559 2842 3200 3467
25 022 022 023 024 617 614 641 661 7381 7741 7734 7355 201 183 181 167 1952 1996 2017 1982
26 030 040 036 047 638 646 611 644 7631 8381 7592 8300 200 186 176 173 1881 1847 1746 1805
27 015 014 012 013 505 465 422 433 6111 5940 5338 4822 170 152 131 134 1540 1373 1368 1291
28 016 013 012 012 466 437 425 368 4837 4339 3786 3072 208 203 201 172 1790 1811 1739 1686
29 011 008 006 006 422 348 290 276 4695 3992 3213 2735 174 165 142 131 1689 1645 1658 1518
30 017 016 015 014 548 513 485 504 6066 6050 5382 5314 221 227 218 204 1640 1495 1485 1375
31 016 014 013 013 551 524 504 420 5722 4948 4442 4039 196 175 162 159 2100 2133 2163 2368
32 029 030 034 038 498 443 395 349 4147 3298 2974 2512 194 179 169 151 1517 1360 1312 1209
33 023 024 027 029 678 695 724 732 7868 8230 8443 8449 255 275 265 294 2745 3142 3492 3808
34 035 040 049 054 782 843 925 979 7755 7821 8166 8865 288 307 344 334 1997 2059 2095 2239
35 012 010 009 008 551 532 565 488 6985 7363 6872 6115 212 219 201 204 1956 2033 1866 1858
36 036 051 060 074 748 821 902 908 8439 8883 9535 11248 240 248 231 245 2820 3191 3766 4238
37 018 016 015 015 431 389 338 303 4107 3434 3157 2520 187 171 151 142 1348 1200 1067 959
38 025 028 030 029 581 611 525 547 5830 4962 4681 4374 253 261 269 270 2207 2307 2331 2511
39 022 024 024 027 682 674 703 690 6289 6707 6188 5735 233 249 244 250 2093 2091 2235 2335
40 025 030 035 033 768 823 834 969 10134 12586 13876 15668 292 311 371 392 2352 2714 2751 2883
41 025 030 027 031 709 722 813 858 8066 8128 9075 8288 271 290 305 319 1724 1582 1602 1558
42 012 010 008 006 296 216 169 151 2310 1435 986 771 150 121 109 090 1253 1054 940 789
43 018 017 016 014 708 740 776 888 8939 10641 10054 10632 273 289 295 316 1937 1932 1994 1938
44 016 014 014 014 417 358 318 288 4373 3420 2897 2543 179 161 145 128 1837 1849 1811 1783
45 024 026 026 026 468 423 393 370 4820 3878 3436 2825 208 203 188 169 1724 1672 1721 1691
46 019 020 020 019 576 637 610 610 6533 6598 6666 6786 204 207 181 181 1946 1988 2078 1928
47 008 005 004 004 308 247 192 177 2637 1898 1408 972 133 105 086 075 1126 925 815 739
48 031 036 038 043 681 774 787 860 5782 6008 6054 5691 259 262 265 272 1842 1834 1805 1657
49 015 015 013 014 588 581 590 582 7612 9229 8377 8573 202 190 182 178 1837 1833 1815 1762
50 016 014 012 010 409 346 296 273 3448 2728 2433 1680 165 158 139 134 1336 1151 989 956
51 022 021 024 022 631 654 628 656 6512 6238 6863 5733 235 237 228 245 2106 2188 2174 2310
52 014 013 011 011 544 544 491 505 6007 5243 4823 4922 188 177 166 155 1851 1899 1771 1806
53 017 016 016 014 796 919 1029 1080 10035 12687 12587 16905 290 319 368 353 2432 2804 2919 3292
54 036 045 051 053 751 805 954 911 7050 7643 7510 7316 268 283 290 317 2151 2212 2363 2417
55 012 009 008 006 353 282 228 208 3268 2446 1989 1481 157 129 112 111 1155 968 849 685
56 016 014 013 014 465 409 371 337 4004 3018 2627 2306 213 212 214 195 1723 1644 1539 1573
57 035 044 057 076 868 941 1085 1127 10866 14632 17431 20089 263 292 289 318 2723 3330 3618 3836
58 025 029 028 032 611 702 664 720 5773 6288 5346 5787 224 215 201 208 1695 1604 1501 1466
59 015 015 013 011 641 694 682 672 6932 7099 7472 6962 253 283 294 282 1955 1988 1919 1987
60 013 012 010 009 370 298 275 241 3023 2238 1947 1441 142 117 100 085 1524 1373 1335 1103
61 022 024 022 025 715 776 784 849 7281 8061 8721 8562 266 276 283 281 2104 2187 2130 2121
62 013 011 011 009 569 530 507 493 5343 4890 4534 4122 232 225 235 244 1432 1283 1141 991
63 013 011 010 008 463 424 354 346 3838 3269 2540 2223 192 178 165 159 1915 1908 1952 1890
64 014 013 011 010 665 685 674 711 7254 7577 8242 7389 276 284 312 343 1902 1877 1784 1881
65 026 029 035 036 791 844 898 1008 9727 10940 12787 13483 301 335 351 371 2458 2857 3022 3126
66 016 015 015 013 456 401 376 341 4018 3011 2673 2147 167 150 131 133 1735 1631 1514 1358
67 023 023 025 028 701 829 848 816 8495 9001 9754 9979 295 315 344 372 1881 1975 1968 1960
68 014 011 010 008 362 280 237 218 3315 2228 1757 1379 172 157 139 125 1503 1351 1309 1081
69 014 014 013 012 469 456 424 396 4095 3235 2716 2469 232 218 235 215 1846 1783 1769 1759
70 019 020 019 018 640 674 670 672 6785 6304 6419 6186 250 247 247 241 2062 2175 2421 2430
71 020 021 023 022 667 677 707 752 8146 8325 8879 8341 237 241 236 242 2093 2275 2232 2274
72 019 016 017 016 645 637 681 608 6432 6267 6364 5536 276 315 309 338 1695 1730 1689 1702
73 022 024 026 026 738 788 831 860 8206 8564 9419 8704 321 362 377 440 2082 2305 2281 2263
74 012 010 009 007 506 470 436 431 5243 4758 4687 3999 194 178 162 148 1729 1608 1574 1632
75 028 030 034 039 705 726 835 795 6408 6759 6094 5739 270 286 301 326 1937 1954 1896 1922
76 023 024 021 021 682 710 721 788 7888 7858 8900 8340 300 338 360 392 2170 2212 2295 2479
77 037 043 046 053 728 846 879 845 8603 8376 9776 10368 237 249 263 261 1994 2163 2185 2264
78 025 025 024 031 675 707 764 751 8609 9940 9632 11128 260 266 267 275 2037 2116 2033 2058
79 028 030 037 038 563 538 538 539 6069 6428 5732 5382 207 198 178 169 1997 2051 2146 2150
80 021 020 024 023 624 649 634 649 5664 5628 4862 4865 249 276 277 279 1991 1978 2042 2079
81 036 049 058 075 682 763 821 821 8688 10002 9662 11595 256 263 267 272 2539 2827 3224 3603
82 025 028 027 031 459 404 360 341 4251 3653 3090 2716 218 209 213 217 2113 2139 2220 2171
83 032 040 045 058 565 534 572 519 5492 5063 4457 4135 200 189 189 176 1395 1244 1164 1068
84 020 018 018 018 624 658 693 620 6792 7087 6486 6822 229 248 240 235 1872 1857 1800 1782
85 019 019 019 018 568 505 491 479 6077 5656 5435 5757 203 183 174 162 1503 1310 1234 1158
86 025 028 029 031 822 902 905 1027 9657 11730 12287 13947 371 443 494 567 2146 2219 2432 2437
87 017 017 017 017 459 442 370 359 4358 3610 2995 2507 211 198 192 184 1850 1834 1860 1886
88 015 015 012 012 521 472 428 410 5694 5653 4597 4234 237 251 240 237 1725 1626 1572 1414
89 017 015 016 016 439 390 356 319 4202 3278 2938 2741 165 140 135 115 1302 1087 949 834
90 029 035 042 044 638 663 758 822 8360 9427 9758 9092 272 294 308 317 2294 2392 2612 2639
91 011 010 008 007 510 496 467 432 5667 5025 4150 4024 221 204 200 193 2037 2135 2235 2228
92 019 019 017 020 468 452 412 386 4681 3784 3476 3030 202 189 181 173 1550 1325 1272 1204
93 009 007 005 004 487 414 388 392 5470 4414 3990 3422 216 208 204 197 1936 1918 2013 2008
94 012 011 009 008 525 506 477 455 5461 5197 4182 3541 207 214 183 193 1749 1664 1613 1559
95 014 012 011 010 653 681 623 720 8745 9344 10628 11302 292 316 329 347 2366 2750 2806 3056
96 016 016 014 014 534 539 504 468 6087 5637 6034 5187 234 241 239 242 1821 1777 1749 1662
97 011 009 007 006 416 329 290 268 3611 2898 2629 1945 186 179 158 148 1560 1397 1335 1168
98 026 031 032 038 703 764 801 853 7465 7940 7971 6489 264 280 283 303 1679 1496 1411 1393
99 021 021 022 021 546 510 479 489 5851 5276 5787 4832 232 222 218 237 1745 1706 1664 1748
100 016 014 012 011 571 562 594 523 6238 6149 6375 6392 261 271 285 290 2084 2148 2302 2267
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1 1 1
2 2 2
3 3 3
4 4 4
1
1
185947613236 209700016612 381289955056 113496909508 105940968217
261410846662 278202957602 636440926272 159777507841 164064511715
340758878594 374684278258 761658671012 238893166595 230844682326
456070302878 505106365162 869191059145 27405034829 302394276185
1 1 1
2 2 2
3 3 3
4 4 4
2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure 2012 Gross Exposure
Sugar Sugar Sugar Sugar Sugar Sugar Sugar
Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas Natural gas
Electricity Electricity Electricity Electricity Electricity Electricity Electricity
Diesel Diesel Diesel Diesel Diesel Diesel Diesel
Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum Aluminum
1
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
USER INPUTS RISK PROFILE OUTPUTS
Date 1112 5 Holistic commodity profile and impact on earnings (baseline 95 confidence band 5 confidence band)
Price and Volatility Base 5th -ile 95th -ile
Q1 Q2 Q3 Q4 2012 Un hedged Hedged Impact Un hedged Hedged Impact
Price Volatility Price Volatlity Price Volatility Price Volatility of hedge of hedge
Sugar ($lbs) $ 020 70 $ 021 70 $ 022 70 $ 023 70 Sales 2069 2069 2069 - 0 2069 2069 - 0
Natural gas ($MMBTU) $ 573 48 $ 573 48 $ 573 48 $ 573 48 Cost of Goods Sold
Electricity ($MWh) $ 6227 69 $ 6227 69 $ 6227 69 $ 6227 69 Fixed Costs 480 480 480 - 0 480 480 - 0
Diesel ($gal) $ 224 37 $ 224 37 $ 224 37 $ 224 37 Sugar 119 58 58 - 0 311 311 - 0
Aluminum ($KTon) $ 1878 32 $ 1878 32 $ 1878 32 $ 1878 32 Natural gas 237 112 112 - 0 351 351 - 0
Electricity 249 87 87 - 0 381 381 - 0
Exposure risk level 95 Diesel 155 101 101 - 0 166 166 - 0
Aluminum 131 91 91 - 0 248 248 - 0
Commodity volumes (in millions) Variable Costs 892 450 450 - 0 1456 1456 - 0
Q1 Q2 Q3 Q4 Total COGS 1372 930 930 - 0 1936 1936 - 0
Sugar (lbs) 1280 1340 1410 1500 Gen amp Admin Exp 360 360 360 - 0 360 360 - 0
Natural gas (MMBTU) 96 101 106 111 EBITDA 337 779 779 - 0 (228) (228) - 0
Electricity (MWh) 10 10 10 10
Diesel (gal) 160 168 176 185
Aluminum (KTon) 16 17 18 19 4 Net commodity exposure 2012 by commodity (in millions $)
Other financial statement assumptions
Q1 Q2 Q3 Q4
Sales volume (in millions) 3200 3360 3528 3704
Sales price ($) 015 015 015 015
Fixed costs ($ millions) 120 120 120 120
GampA Exp ($ millions) 90 90 90 90
Hedge Selection
Futures
Sugar No
Natural gas No
Electricity No
Diesel No
Aluminum No
3 Gross commodity exposure 2012 (in millions $)
a) By quarter b) By commodity
All exposures reported as the difference between the 95th percentile highest exposure and the expected baseline gross commodity exposure
2 Estimated commodity volumes for 2012 by quarters (variable units in millions $)
1 Commodity price projections (baseline 95 confidence band 5 confidence band)
Oliver Wyman Six Steps to Assess Commodity Risk Exposure
Read Me - File Overview
Page 7: Commodity Risk Management - Oliver Wyman
Page 8: Commodity Risk Management - Oliver Wyman
Page 9: Commodity Risk Management - Oliver Wyman
Page 10: Commodity Risk Management - Oliver Wyman
Page 11: Commodity Risk Management - Oliver Wyman
Page 12: Commodity Risk Management - Oliver Wyman
Page 13: Commodity Risk Management - Oliver Wyman
Page 14: Commodity Risk Management - Oliver Wyman
Page 15: Commodity Risk Management - Oliver Wyman
Page 16: Commodity Risk Management - Oliver Wyman
Page 17: Commodity Risk Management - Oliver Wyman
Page 18: Commodity Risk Management - Oliver Wyman
Page 19: Commodity Risk Management - Oliver Wyman
Page 20: Commodity Risk Management - Oliver Wyman
Page 21: Commodity Risk Management - Oliver Wyman
Page 22: Commodity Risk Management - Oliver Wyman
Page 23: Commodity Risk Management - Oliver Wyman
Page 24: Commodity Risk Management - Oliver Wyman
Page 25: Commodity Risk Management - Oliver Wyman
Page 26: Commodity Risk Management - Oliver Wyman
Page 27: Commodity Risk Management - Oliver Wyman
Page 28: Commodity Risk Management - Oliver Wyman
Page 29: Commodity Risk Management - Oliver Wyman
Page 30: Commodity Risk Management - Oliver Wyman
Page 31: Commodity Risk Management - Oliver Wyman
Page 32: Commodity Risk Management - Oliver Wyman
Page 33: Commodity Risk Management - Oliver Wyman
Page 34: Commodity Risk Management - Oliver Wyman
Page 35: Commodity Risk Management - Oliver Wyman
Page 36: Commodity Risk Management - Oliver Wyman
Page 37: Commodity Risk Management - Oliver Wyman
Page 38: Commodity Risk Management - Oliver Wyman
Page 39: Commodity Risk Management - Oliver Wyman
Page 40: Commodity Risk Management - Oliver Wyman
Page 41: Commodity Risk Management - Oliver Wyman
Page 42: Commodity Risk Management - Oliver Wyman
Page 43: Commodity Risk Management - Oliver Wyman
Page 44: Commodity Risk Management - Oliver Wyman
Page 45: Commodity Risk Management - Oliver Wyman
Page 46: Commodity Risk Management - Oliver Wyman
Page 47: Commodity Risk Management - Oliver Wyman
Page 48: Commodity Risk Management - Oliver Wyman