weather derivitives presentation 2009

8/8/2019 Weather Derivitives Presentation 2009

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Weather DerivativesTroy Houston

Bob ReavyMatt Schreurs



Weather DerivativesFinancial instruments used to reduce risksassociated with adverse weather conditions

Weather Risks involve uncertainty in cashflows caused by weather events: ± Temperature, Rainfall, Snowfall, Frost,

Hurricanes, etc.

Nearly 1/3 of U.S. economy is directlyaffected by weather



History1996 - First weather derivative deal was in July between AquilaEnergy and Consolidated Edison Co. ± ConEd purchased electric power from Aquila for the month of August ± Weather clause was embedded into the contract

± Aquila would pay ConEd a rebate if August was cooler than expected

1997 - Derivatives began trading over-the-counter

1999 - Chicago Mercantile Exchange introduced the first exchange-

traded weather futures contracts (and corresponding options)

Currently - CME trades weather derivative contracts based ontemperature, hurricanes, frost, and snowfall



History of Volume Traded

March 2008 - over 436,000 contracts valued at over 20 BillionThe number of contracts has been trending upward



Users of Weather DerivativesRisk Holder Weather Risk Specific Risk

Energy Companies Temperature Lower sales duringwarmer winters & cooler summers

ConstructionCompanies

Temperature/Snowfall/Rain Lower sales ±when theycan¶t work

Ski Resorts Snowfall Lower sales with lesssnowfall

Agricultural Temperature/Snowfall/Rain Crop losses due toextreme temperatures



Temperature Derivatives

Heating Degree Days (HDDs) ± Cumulative amount by which the avg. daily

temperature is below 65 F. ± Energy is needed to heat

Cooling Degree Days (CDDs) ± Daily temperature is above 65 F ± Energy is need to cool



Temperature ContractsContract Size: $20 times CDD/HDD index

CDD months: Apr, May, June, July, Aug, Sep, Oct.

HDD months: Oct, Nov, Dec, Jan, Feb, Mar, Apr

Cash Settlement

Location: 24 US cities



Measuring Index Values

HDD

Monthly Index ± Sum of daily HDDs

D ay Hi Low Avg. H DD

Monday 54 34 44 21

Tues. 52 37 45 20

Wed. 61 30 46 19

Thurs. 45 29 37 28



Example 1 ± Short Position

Chicago Utility company wants to hedge amild July

July CDD Index for Chicago= 800Risk = $1,000 every degree changeHedge: Sell 50 CDD futures (1,000/20)

Scenario 1: July is mild, CDD = 700:Scenario 2: July is hot, CDD = 900:



Scenario 1

July is mild, CDD=700 ± Revenues down

100 x -$1,000 = -$100,000

± Gain on Futures:100 x $20 = $2,000/contract$2,000 x 50 contracts = $100,000



Scenario 2

July is hot, CDD = 900 ± Revenues increase

100 x $1,000 = $100,000

± Loss on Futures-100 x $20 = -$2,000/contract-$2,000 x 50 contracts = -$100,000



Standardized Risks

Spatial Risk ± Reference Weather Index may differ from

location of interest ± Ex: Bloomington farmer

Technological Risk ± Relationship between weather and volume ± May or may not be linear



Other Types of Wx Derivitives

Hurricanes ± Hurricane ± Hurricane Seasonal ± Hurricane Seasonal MaximumSnowfall ± Snowfall Monthly ± Snowfall Seasonal

Frost ± Frost Monthly ± Frost Seasonal



Hurricane DerivativesAvailable for US Hurricane Season ± Season runs from Jan 1 to December 31

± Landfall = Texas to Maine ± ³Cat-in-the-Box´ = Oil fields in GoM

Contracts available ± Events ± Seasonal ± Seasonal Maximum

Hurricane futures are binary contracts



What are Binary Contracts?Binary contracts are contracts with only

two outcomes

± $Big Money$ ± Zero

If contract crosses threshold it pays out,otherwise it pays nothing ± Contract price = (expected likelihood of event)

X (payout if event occurs)



Hurricane Derivatives

(Continued)Events ± CHI value of one particular named hurricane

that occur during the season

Seasonal ± Total CHI value of all named hurricanes that

occur in specific region during the season

Seasonal Maximum ± CHI value of the strongest hurricane that

occurs in region



Hurricane Derivatives

(Continued)Regions include ± Gulf Coast

± Florida ± South Atlantic Coast ± Northern Atlantic Coast ± Cat-In-A-Box (Oil Fields)

Contracts-$1,000Settlement-CME Hurricane Index



Hurricane Derivatives-Example

Problem: Oil Co needs to protect Gulf of Mexico (GOM) oil rigs

Risk: Hurricane damage to oil rigsSolution: ± Purchase Seasonal Maximum contract

Number of contracts (Value of potentialrevenue loss due to damage to Rig/1K)

Contract index number based onvulnerability of oil rig



Hurricane Derivatives-ExampleIndex Number ± Ranges from 1-30 ± Katrina was a 20.4

Oil Co buys index number thatcorresponded to lost income due to thedestruction of the rig

Outcome(s): ± Hurricane destroys rig = binary payday ± No hurricane or smaller ones = rig is still intact

and no lost revenue



Hurricane Derivatives-

ParticipantsHedgers ± Insurance Companies

± Oil Companies ± Tourist Destinations ± Commercial Property Owners

Speculators



Snowfall DerivativesAvailable for the following cities: ± New York LaGuardia Airport ± Chicago O'Hare International Airport ± Minneapolis/St. Paul Airport

± Detroit Metro Airport ± New York Central Park ± Boston Logan International Airport

Contracts for individual months or entire season

Monthly contracts run from November through April but trades throughout the year Contracts- $500 per inchSettlement- CME Snowfall Index



Snowfall Derivatives

(Continued)All contracts are settled in cashMaximum position is 10,000 contracts

Options are also available



Snowfall Derivatives-

ParticipantsHedgers ± City Governments

± Ski Resorts ± Rock Salt Companies ± Airlines?

Speculators



Ex: SnowfallSki Resort has risk of low snowfallSolution: Buy Snowfall put optionScenario 1: Snow fall is lower than strikepriceThe put option increases in value, butlower snowfall reduces ski resort¶srevenuesScenario 2: Snow fall is above strike priceThe put option is worthless, but resortdoesn¶t lose customers due to level of

snowfall



Frost Derivatives

Only traded for one city: Amsterdam-Schiphol, Netherlands

Traded in EurosContract Size: 10,000 Euros times CMEFrost Index

Months available: November - February



Frost Derivatives-ParticipantsCrazy Europeans (No offense intended Dr. Bouriaux)

Hedgers

Speculators



2008 CME Group Survey of CFO¶s

21% say their company is ³highly exposed´ to Wx volatilityAnother 38% are ³very exposed´35% of energy companies have used vs. 10% for other industries51% say their company is not prepared to cope with Wx volatilityOver 50% have not even tried to determine Wx risk exposure eventhough they hedge against interest rate, currency, and commodityrisk; about the same number contend it wouldn¶t matter if they didsince Wx risk can¶t be managedOf the companies that have used them, 86% say they were usefuland 72% will use again



Other Uses (Examples)

2007 retailers blamed weather for poor earning

Airlines losing sales due to lack of accessto airports. Heavy rainfall in TX in 2007caused on airline to lose 100 million inrevenueHeavy snowfall in Northeast last year. Itcost NYC $1 million per inch to removesnow in direct costs.



Pricing Models/Techniques

OverviewSimple Gaussian Model

Brownian MotionBlack-ScholesBurn Analysis

Monte Carlo Simulations



Before We Get Too Carried Away...

No single pricing approach defined as theµstandard¶ for Wx derivatives

Many accepted approaches exist, groupedinto two categories: ± Statistical ± mainly forward looking. ± Historical ± ³History Repeats´, use the past to

predict the future.



Simple Gaussian Model

dr = a(i)*(b(i)-r) dt + v(i) dzWhere

r is the current day¶s temperature

dr is the instantaneous change in r dt is an infinitesimally small unit of timeb(i) is the mean temperature on day ia(i) is the speed of mean reversion on day iv(i) is the volatility on day idz is a Weiner process based on a normal distribution with a mean of zeroand a standard deviation of 1a(i) is allowed to change over time due to the seasonality of weather



Brownian Motion

Alone, Brownian Motions do not determinethe price of Wx derivativesBrownian Motions are among the simpleststochastic processesStochastic processes are a mathmaticalway of introducing ³randomness´ to an

equationAre often used in conjunction with other models



Black-Scholes««.or not.Main assumption of Black-Scholes, the µrandom walk¶, is

often not present in Wx Derivatives. ± Temperatures in certain locales are subject to relative limits ± Assets (think stocks) can be priced anywhere from $0 to infinity

More often, mean-reversion takes place as temperaturesusually float around an average.Payoff ± Black-Scholes option payoff is determined by the underlying

asset¶s value at maturity. ± Wx Derivatives are often referred to as µAsian¶ or average price

options. Payoffs are often capped as well.



Burn Analysis

Used to determine if a position taken todaywould have yielded a profit historically.

Makes a strong assumption that pastevents/results will repeat themselves.Used with HDD and CDD as historicaltemperatures are easily retrieved.



Monte Carlo Simulations

Run a series of simulations against a statisticallyderived model and determine expected returns.Based on what you¶re simulation, important totake mean-reversion into consideration.Average Return Equation to Solve:

where t represents time and represents theseries of calculation points.



Pricing Summed Up

No ³magic bullet´ for Wx derivative pricingGenerally, the historical models are

preferred ± May be due, in part, to the fact that theunderlying ³asset´ is an average based onhistory«.



References

http://www.docstoc.com/docs/34688442/Weather-Derivatives-Pricing-and-Risk

http://www.derivativesstrategy.com/magazine/archive/2000/0300col1.asphttp://www.fea.com/resources/pdf/a_weather_derivatives.pdf http://www.cmegroup.com/trading/weather



References

Brockett, Patrick, Wong Mulong, &Chuanhou Yang. (2005). Weather Derivatives and Weather RiskManagement. Risk Management and Insurance Review . 8(1), 127-140.Carabello, Felix. Introduction to Weather Derivatives. Investopedia . Retrieved fromhttp://www.investopedia.com/articles/optioninvestor/05/052505.asp



References

Jones, Travis. (2007). Agricultural Applications of Weather Derivatives.International Business & Economic Research Journal . 6(6).Lennep, David, Teddy Oetomo, MaxwellStevenson, & Andre De Vries. (2004).Weather Derivatives: An Attractive

Additional Asset Class. The Journal of Alternative Investments. 7(2). 65-74.



References

Manfredo, Mark & Richards, Timothy.(2009). Hedging with Weather Derivatives:

A Role for Options in Reducing BasisRisk. Applied Financial Economics . 19, 87-97.

weather derivitives presentation 2009

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