resolving the credit risk conundrum

8/10/2019 Resolving the Credit Risk Conundrum

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Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Capital IQ.

Not for distribution to the public. Copyright 2014 by Standard & Poors Financial Services LLC (S&P). All rights reserved.

Marcel HeinrichsCo-Speaker

Director, Business Development, S&P Credit Solutions

S&P Capital IQ

Mark WilliamsCo-Speaker

Executive-in-Residence/Master Lecturer, Finance Department

Boston University School of Management

Alma Chen - Moderator

Regional Head Americas, Analytic Development Group

S&P Capital IQ

October 28, 2014

Resolving the Credit Risk Conundrum:Fundamental Analysis vs. Market Signals


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2 Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Capital IQ. Not for distribution to the public.

Todays Speakers

Marcel Heinrichs

Director, Business Development

S&P Credit Solutions

S&P Capital IQ

Mark Williams

Executive-in-Residence/Master Lecturer

Finance Department


Alma Chen

Associate Director

Analytics Development

S&P Capital IQ

(Moderator)

Please note: The views and opinions expressed by Mr. Will iams do not necessarily reflect the opinion of S&P Capital IQ and its affiliates.


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Introduction: Where credit risk matters

Current challenges in credit risk management and surveillance

Navigating the credit landscape via the spectrum of credit measures

Introducing the spectrum of credit measures

Key differentiating factors between the metrics within the spectrum

Market signals of credit risk

Fundamental measures of credit risk

The case for utilizing both market signals and fundamental measures of

credit risk

Case study and Summary

Collaboration of S&P Capital IQ with the academic sector

Topics Of Discussion

I

II

III

IV

VI

V


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What Is Credit Risk?

Narrow definition: risk that a borrower will default on its issued debt

Wider definition: risk that a business partner cannot fulfil financial obligations

Examples:

Loss of interest payments and principal

Loss in investment

Disruption to cash flows

Increased collection costs

Potential bankruptcy

Need for Regulatory Reporting

Business disruption

I


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Who Needs Credit Risk Solutions?

Loan Origination

Credit Department

Risk Management

Debt Capital Markets Structured Finance

Loan Syndication

Ratings Advisory

Leveraged Finance

Restructuring

Idea Generation Pre-Trade

Credit Analysis Pre- and Post-Trade

Portfolio and Performance Risk

Management

Underwriting

Credit Analysis

Risk Management

CORPORATE

A Commercial/Trade Credit

Supply Chain

Transfer Pricing

Captive Finance

CORPORATE

B

(INVESTMENT) BANK

INSURERASSET /INVESTMENT

MANAGER

COMMERCIAL

LENDER

NON-FINANCIAL CORPORATIONS

I


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Period of 2001-2013; entire universe of publicly rated companies by Standard & Poors Ratings Services,

that are also listed at a stock market

7316 companies, of which 200 defaulted on their issued debt

Assess all companies with two different kind of models; for defaulters exactly one year prior the actual default.

One model is based on fundamental data, the other is based on stock price volatility as a market signal

generate the following matrix of observed default rates per bucket;

observed default rate = number of defaulters / total number of entities

Different IndicatorsDifferent Perspectives

Highercreditrisk,

asindicatedbyfundam

entals

Higher credit risk,

as indicated by market signals

Source: S&P Capital IQ.

0.00 to

0.01%

(aaa to aa-)

0.01 to

0.03%

(aa+ to aa-)

0.04 to

0.13%

(a+ to a-)

0.13 - 0.63%

(bbb+ to bbb-)

0.63 to

2.27%

(bb+ to bb-)

2.27 to 9.64%

(b+ to b-)

>9.64%

(ccc+ or worse)

aaa 0.00 0.00 0.00 0.00 0.00 N/A N/A

aa+ to aa- 0.00 0.00 0.00 0.00 0.00 0.00 0.00

a+ to a- 0.00 0.00 0.00 0.00 0.00 0.00 0.00

bbb+ to bbb- 0.00 0.00 0.03 0.15 0.18 0.75 1.19

bb+ to bb- 0.00 0.00 0.15 0.22 0.62 1.44 4.78

b+ to b- 0.00 0.28 0.46 1.15 3.35 6.07 10.89ccc+ or worse N/A 0.00 0.00 6.19 11.43 20.00 28.53

Market Signals-Based Model (Merton-Type Approach)Fundamentals-Based Scoring

Model

I


7/307 Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Capital IQ. Not for distribution to the public.

Rated: Wealth of Information

Banks ~6,500 &

Corporations ~3,500

Publicly Listed Companies

~60,000 Banks &

Corporations (Active)

Private: Information Scarcity

Banks (Est.) ~50,000 &

Corporations (Est.), Millions

Problem I: Large Unrated Counterparty UniverseII

Banks and corporations engage in business transactions with counterparties thatpresent: limited or unavailable information, and/or unreliable credit assessments

Source: S&P Capital IQ. Data as of June 10, 2014.



Problem II: Complex Global Credit Matters And Different Credit Signals

Source: The Economist, April 21, 2012.

Ratings stable but

stock price down

and CDS spread up 14,000Suppliers

from around

the world

Investing in

emerging markets

Poor You

Source: S&P Capital IQ, May 14, 2014.

II



Convention IType Of Model Output

Scoring Models

Scoring models produce a credit score (lower case letter grade such as bbb-), which is then also

mapped to a Probability of Default (PD). However, the primary output and main interest of its users is

the credit score as a qualitative measure of credit risk

Developed on ratings(full scale from AAA to D = default) or similar assessments such as shadow

ratings, credit estimates etc.

Favored by clients with an affinity to ratings, usually with a background as a fundamental credit

risk analyst

Input DataRatings and

explanatory factors

State-of-the-art

Modeling Recipe

Output DataCredit scores in

lower case letters

a- bbb+ ccc bb+

b- aaa aa-

Cash

EBITDA

Total Assets

Debt/Capital

AA+ BBB- CCC BB

AA- A- B CCC+

Proprietary

Algorithm

Lower case letters indicate that these credit risk

assessments are derived quantitatively by S&P

Capital IQ and NOT by rating analysts fromStandard & Poors Ratings Services

III



Convention IType Of Model Output

PD Models

PD models produce a PD in the first place, which is then also mapped to a credit score. The primary

output and main interest of its users is the PD as a quantitative measure of credit risk

Developed on default data (binary decision: either a company defaulted on its debt repayments in a

particular year or not)

Favored by clients who are not used to ratings as a rank measure or who do not believe in the

relevance of ratings, and often have a quantitative background

Input Data

Default flags and

explanatory factors

State-of-the-art

Modeling Recipe

Output Data

Default probabilities

0.26% 1.59%

29.64% 0.05%

0.46% 1.21%

Cash

EBITDA

Total Assets

Debt/Capital

0 0 1 0

0 0 1 0 1 0 0

1 0 1 1 0 0

Proprietary

Algorithm

III



Convention IIType Of Explanatory Factors

Fundamental Data:Any data that is usually collected at periodically, often annually or

quarterly (in rare cases monthly)

Firm-specific financials (Annual report, quarterly financial statements)

Systemic Risk factors such as

Macroeconomic factors (such as GDP growth, inflation rates)

(other) Factors that reflect the environment that a company operates in vis-a-vis country risk, industry risk or

sovereign risk

Market Signals:Any data that is usually collected at high frequency, most often daily or even

intra-daily

CDS spreads of companies whose credit risk is traded in the CDS market

Fixed Income spreads of companies that issue debt via bonds or similar instruments

Stock market volatility of public companies

Since ratings are based on fundamental data, anyone with an affinity to ratings tend to

favor models that are based on fundamental data

Anyone that find ratings less relevant tend to favor models that rely heavily (or solely)

on market signals

III



Mid- to Long-Term

Many

Fundamentals-

Based Models

Convention IIITime Horizon

Point in Time (PIT) Snapshot of the current market opinion: Used as a meansto screen out potential defaulters. These can include falsepositives, but are unlikely to omit companies that can

potentially default

Short- to Mid-Term Useful for someone who wants something less volatile thanPD market signals, but more volatile than Ratings

Such models are favored by users with an affinity to pure

quantitative risk measures for pricing, reserve

calculation, Credit VaR etc

For ratings, these are expected to be stable for 3-5 years forinvestment grade (IG) and 2-3 years for non-IG companies

Much less volatile results

TimeHorizon

Shorter/more

volat

ile

Longer/Le

ss

volatile

CDS

spreads

Hybrid Models (out

of scope for this

presentation)

Stock

Price

Volatility

Public

Ratings

Bond

spreads

III



The Complete Picture: The Credit Spectrum

Issuer Credit Ratings

Daily Monitored

Quantitative Fundamentals-Based Models

Quarterly Updates

Quantitative Market Signals Models

Daily Updates

Qualitative Judgment Peer Group AnalysisFundamentals

Scoring or PD ModelStock Price

VolatilityBond Spread CDS Spread

Usual

Primary

Measure

Credit ratings (BBB-)*Rank Peers from

Top to Bottom

Credit Score (bbb-)

Then mapped to PD in %

OR vice versa

PD in %

Then mapped to

credit score

PD in %

Then mapped to

credit score

PD in %

Then mapped to

credit score

Coverage

Global Coverage

~9k companies

Global Coverage

Unlimited applicability

Global Coverage

Unlimited applicability

Publicly listed

Companies

38k companies

Companies w/

liquid bond market

~6k companies

Companies w/

liquid CDS market

>1k companies

TimeHorizon Medium to

long- term metricMedium tolong-term metric

Medium tolong- term metric

Point-in-Timemetric

Point-in-Timemetric

Point-in-Timemetric

78% of companies stay at

same level after 1 year76% of companies stay at same level after 1 year 32% of companies stay at same level after 1 year

*From Standard & Poors Ratings Services. S&P Capital IQ, as well as its products and services are analytically and editorially separate and independent from other analytical

areas at S&P, including S&P Credit Ratings.

Fundamentals-based quantitativemodels expand the rated

universe to any public or private

company around the globe for a

medium- to long-termview of

credit risk

Market signals modelsprovide additional short-

term(point-in-time) credit

risk indicators

III



Conclusion:

Market Signals PD Have Lower Type I Errors in the short-term

We can use these as a first cut to shortlist potential defaulters

Why Use Multiple Indicators Of Credit Risk?

Source: Bankruptcy and default data from SP

CreditPro, CreditModel Scores from S&P

Credit Analytics, Market Signals PD from S&PCapital IQ, from 2001 to 2013.

For illustrative purposes only.

Frequency Distribution Of Defaulters

In this example, we classified all companies with a Market Signal PD < 9.64%,or a CreditModel score of b- and above, as healthy companies.

Type I Error:

Number ofdefaults in

healthy group /

Total Number of

Defaults

Detected too late:

lose moneybecause of wrong

acceptance of

business

engagement

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

aaa aa a bbb bb b ccc &below

CreditModel Score PD Market Signals

Smaller area below the blue

line than the red line in the

shaded area

IV



0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

aaa aa a bbb bb b ccc &below

CreditModel Score PD Market Signals

Why Use Multiple Indicators Of Credit Risk?

Frequency Distribution Of Non-defaulters

Smallerarea below

the red line

than the

blue line in

the shaded

area

In this example, we classified all companies with a Market Signal PD > 9.64%,

or a CreditModel score of ccc+ and below, as unhealthy companies.

Conclusion:

CreditModel scores have lower type II errors in the medium- to long-term

First, shortlist potential defaulters using PD Market Signals, then use CreditModelscores to narrow down the list of potential defaulters

Source: Bankruptcy and default data from SP

CreditPro, CreditModel Scores from S&P Credit

Analytics, Market Signals PD from S&P CapitalIQ, from 2001 to 2013.

For illustrative purposes only.

Type II Error:

Number of non-defaulters in bad-

companies group

/ total number of

healthy

companies;

False alarms: losemoney because of

wrong rejection of

business

engagement

IV



0

1

2

34

5

6

7

8

9

10

11

12

13

1415

16

17

18

19

20

21

22

23

24

25

MDS CDS

Scores

0

1

2

34

5

6

7

8

9

10

11

12

13

1415

16

17

18

19

20

21

22

23

24

25

PD Model Fundamentals

Credit risk of company as indicated by different credit signals

British Petroleum (LSE:BP.)s

share price fell and its CDS spiked

during the oil spill in year 2010 Volatile equity or CDS based

market signals would have

indicated a need to place BP on a

watch list.

Company did not default on its

debt, but contemplated filing for

bankruptcy/reorganization in

August 1, 2012

The Credit Surveillance Conundrum

Source: S&P Ratings, S&P CreditModel Scores, and PD Market Signals from S&P Capital IQ RatingsDirect, October 2008October 2013.

Key Developments news from S&P Capital IQs news sources.

0

1

2

34

5

6

7

8

9

10

11

12

13

1415

16

17

18

19

20

21

22

23

24

25

Standard & Poors Ratings

AAA / aaa

AAA+ / aa+

AA / aa

AA- / aa-

A+ / a+

A / a

A- / a-

BBB+ /

bbb+

BBB / bbb

BBB- / bbb-

BB+ / bb+

BB / bb

BB- / bb-

B+ / b+

B / b

B- / b-

CCC+ / ccc+

CCC / ccc

CCC- / ccc-

CC / cc

C / c

SD/D/NR

/sd/d/nr

Case StudyWill This Company Default On Its Debt?V



Company Industry Country CreditModel Score PD Market Signals

OSX Brasil S.A. O&G Equipment and Services Brazil cc 53.90% (cc)

Doral Financial Corp Mortgage Finance Puerto Rico cc 40.89% (cc)

Air Berlin PLC Airlines Germany ccc 22.46% (ccc-)

PT Bumi Resources Tbk Coal and Consumable Fuels Indonesia cc 20.42% (ccc-)

Caesars Entertainment Corp Casinos and Gaming US ccc- 16.15% (ccc)

Petrobras Argentina SA Integrated Oil & Gas Argentina ccc- 16.02% (ccc)

Double-Red Flag Candidates Around the Globe from Various Sectors as of Sep 30, 2014 (Excerpt)

0.00 to 0.01%

(aaa to aa-)

0.01 to 0.03%

(aa+ to aa-)

0.04 to 0.13%

(a+ to a-)

0.13 - 0.63%

(bbb+ to bbb-)

0.63 to

2.27%

(bb+ to bb-)

2.27 to 9.64%

(b+ to b-)

>9.64%

(ccc+ or worse)

aaa 0.00 0.00 0.00 0.00 0.00 N/A N/A

aa+ to aa- 0.00 0.00 0.00 0.00 0.00 0.00 0.00

a+ to a- 0.00 0.00 0.00 0.00 0.00 0.00 0.00

bbb+ to bbb- 0.00 0.00 0.03 0.15 0.18 0.75 1.19

bb+ to bb- 0.00 0.00 0.15 0.22 0.62 1.44 4.78

b+ to b- 0.00 0.28 0.46 1.15 3.35 6.07 10.89

ccc+ or worse N/A 0.00 0.00 6.19 11.43 20.00 28.53

PD Model Market Signals (%)

CreditModel Score

Which Companies Are To Be Watched Now?

Source: S&P Capital IQ.

V


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Summary I

1. Always remember:

2. On a standalone basis, no single credit risk model is superior to another

across the entire range of performance measures or criteria.

3. In particular, between fundamentals-based and market signals-based models

there is a trade-off between

Type 1 errors (accepting bad customers): market signals-based models are superior in

detecting (rapid) credit deterioration &

Type 2 errors (rejecting good customers) : fundamentals-based models are superior in

avoiding more false alarms

It is critical to know the type 1 and type 2 % of your model

Investors need to decide which error they deem more important

Essentially all models are wrong, but some are useful

[George E.P. Box]

Market Signals-

Based PD

Models

Fundamentals-

Based

Credit Scoring

or PD Model

Issuer Credit

Ratings

V


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Summary II

0.00 to 0.01%

(aaa to aa-)

0.01 to 0.03%

(aa+ to aa-)

0.04 to 0.13%

(a+ to a-)

0.13 - 0.63%

(bbb+ to bbb-)

0.63 to

2.27%

(bb+ to bb-)

2.27 to 9.64%

(b+ to b-)

>9.64%

(ccc+ or worse)

aaa 0.00 0.00 0.00 0.00 0.00 N/A N/A

aa+ to aa- 0.00 0.00 0.00 0.00 0.00 0.00 0.00

a+ to a- 0.00 0.00 0.00 0.00 0.00 0.00 0.00

bbb+ to bbb- 0.00 0.00 0.03 0.15 0.18 0.75 1.19

bb+ to bb- 0.00 0.00 0.15 0.22 0.62 1.44 4.78

b+ to b- 0.00 0.28 0.46 1.15 3.35 6.07 10.89

ccc+ or worse N/A 0.00 0.00 6.19 11.43 20.00 28.53

PD Model Market Signals (%)

CreditModel Score

Safe

Haven

Stay

Away

4. Superior performance can be achieved by leveraging two independently derived

signals - one being fundamental and one being market-driven - and focusing on

companies that give double confidence:

5. For companies with mixed signals, follow the suggested approach in our paper:

http://bit.ly/1zelpbO

V


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Current Projects with Academics

Standard & Poors Ratings Services, S&P Dow Jones Indices and S&P Capital IQ are

engaged with the academic sector in order to continuously provide best-in class data

and analytics both for research and for any of our customers immediate workflows.

Examples of current credit risk projects include:

Analysis of discriminatory power of behavioral data in credit risk with MSc students from Columbia

University; students get credit for this project as part of their curriculum

Project with world-renowned professor of economics from NYU on analysis of ratings momentum

Speaking engagements in credit risk to academics and/or (financial engineering) students from various

universities in the U.S. and the UK Independent review of our suite of credit risk models by academics from top university in Far East

Well established program of internships in fall with MSc students in financial engineering from University

of Berkeley

Distribution of our data and research articles via WRDS (Wharton Research Data Services)

WE HAVE PLENTY OF IDEAS FOR

RESEARCH IN CREDIT RISK AND ARE

LOOKING FORWARD TO HEARING FROM

YOU ON ANY SUGGESTIONS FOR

FUTURE COLLABORATIONS

VI


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What Else Matters

1. Parental Support for Subsidiaries or Governmental Support for

Government-Related Entities (GREs)

Frances national railway service SNCF is a GRE and has a standalone rating of BBB-,but its final rating is AA- (6 notches up!) because of its criticality to Frances

infrastructure system. Frances sovereign rating is AA

Petrobras Argentina gets one notch uplift for assumed support from its parent company

in Brazil

2. Systemic Risk Factors Country Risk

Sovereign Risk

Industry Risk

Economic Risk

3. Recovery Risk (when a default occurs)

Distinguishes risk at issuance (or facility) level, while default risk is assessed at

company-level

4. So much more


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Q&A

Marcel Heinrichs

S&P Capital IQ

Mark Williams


Alma Chen

S&P Capital IQ

(Moderator)

Please note: The views and opinions expressed by Mr. Will iams do not necessarily reflect the opinion of S&P Capital IQ and its affiliates.


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Biographies

Marcel Heinrichs, CFA

Director, Business Development, S&P Credit Solutions, S&P Capital IQ

Marcel is responsible for the market development of credit risk offerings to financial institutions and non-financial corporations in the Americas. In this role, Marcel defines

the roadmap for new offerings of content, tools and analytics, works with marketing and sales teams on activities for branding and sales generation, oversees thought

leadership and interaction with key market influencers including top clients, regulators or associations and paves the path for new markets and client segments. Prior to his

current role, Marcel was global head of the Analytic Development Group (ADG) of S&P Capital IQ, responsible for the analytical innovation, development, maintenance andongoing validation of all credit risk models and products. Until 2010, Marcel was based in London and co-leading the services team of S&P Risk Solutions EMEA, the

consultancy business of S&P Capital IQ. Before joining S&P Risk Solutions in 2004, Marcel taught courses in econometrics, financial econometrics, mathematicaleconomics and macroeconomics at the London School of Economics and consulted various financial institutions on a variety of modeling problems. Marcel is also a

member of the Financial Markets Group, the Research Center in Finance of the London School of Economics. He has a Master degree in economics from the University of

Bonn, Germany and Ecole Nationale de la Statistique et de lAdministrationEconomique (ENSAE), France.

Mark Williams

Executive-in-Residence/Master Lecturer, Finance Department, Boston University School of Management

Mark is an academic, author, columnist and risk management expert. Prior to joining Boston University he worked as a trust banker, senior trading floor executive and as a

Federal Reserve Bank examiner. Since 2002, he has been on the finance faculty at Boston University specializing in banking, energy and capital markets related matters.

He teaches at the graduate and undergraduate levels. In 2008 he was awarded the Boston University Beckwith Prize for excellence in teaching. Mark frequently appears inthe national media and has been a guest columnist for the Financial Times, New York Times, Reuters.com, Forbes.com, Business Insider, Boston Globe and Foreign Policy

Magazine. In 2010, his book Uncontrolled Risk, detailing the rise and fall of Lehman Brothers and root causes of the financial crisis was published by McGraw Hill.

www.uncontrolledrisk.com. In 2013 he coauthored Longwood Covered Courts and the Rise of American Tennis. This work won a best book award at the New England

Book Show. In 2014 he provided Congressional testimony relating to the risks associated with virtual currencies. In addition to teaching and expert witness work, he

services on several boards including Appleton Partners LLC, a Boston-based, wealth-management company and Standard & Poors Academic Advisory Council. Mark

holds a BSBA in Finance from the University of Delaware and a MBA from Boston University. He is also a founding board member of the Boston Chapter of the Global

Association of Risk Professional, a member of the Boston Analyst Security Association and International Association of Financial Engineers.

Alma Chen

Associate Director, Analytics Development, S&P Capital IQ

Alma is Head of the Analytic Development Group (ADG), Americas, and is currently based in New York. (Formerly, Head of ADG APAC, based in Hong Kong.) Her team isfocusing on analytical development, maintenance and ongoing validation of credit risk models and products, which are used by financial institutions and other credit-

sensitive entities to measure and manage credit risk, also within regulatory frameworks such as Basel II/III or Solvency II. Her team provides analytical support to existing

clients and Sales during pre-sales support, as well as to Risk Solutions, for ad-hoc assignments in Americas Region and APAC respectively. She has more than 12 years of

experience in the risk analysis and financial modeling. Prior to joining S&P Capital IQ, Alma was a Lead Consultant, who has provided robust and accurate solutions on

credit risk quantitative & expert-judge hybrid models for key components of Expected Loss: Probability of Default, Loss Given Default & Exposure at Default, including

different stages of modeling cycle: development, calibration, performance monitoring, validation and optimization. Before moving to Asia in 2008, Alma worked as an

economist of a U.S.-based company engages in mortgage purchasing, credit guarantee, issuing guaranteed mortgage-related securities and portfolio investment activities,

where Alma accumulated seven years of extensive experience in financial model development, validation and calibration, Alma also conducted economic research and

analysis. Alma holds a Masters Degree in Statistics from Texas A&M University in the United States, and a Bachelors Degree from Tsinghua University in China.


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Appendix


25/30


Fundamentals-Based ModelsStrengths And Weaknesses

* Available for Risk Scorecards only.

Key Strengths:

Models which are validated on a regular basis can be calibrated to maintain high

forecasting accuracy

Ties in company fundamentals to business and financial risk

Can be used for private companies where there are no traded equities, bonds, or CDS

Hybrid qualitative + quantitative models* can include the impact of government / parent

company support and qualitative factors (e.g., management quality) on credit risk

Key Weaknesses:

Unable to detect changes in fundamentals between reporting periods

May react too slowly for equity investors and for fixed income investors with

short holding horizons


26/30


Market Signals-Based ModelsStrengths And Weaknesses

Bonds-drivenEquity-driven CDS- driven

Strengths

Weaknesses

Covers all publicly listed

companies, including

emerging markets

Covers companies that

issue bonds

This is the market price of the

entitys credit risk where CDS

is traded

Research shows that CDS

provide additional information

on credit risk that is notreflected in distance to default

Particularly suited for

sovereign credit risk

monitoring

May be noisy

Equity prices can react to

non-credit related events

Equity prices can over-react

to news, and exhibit short-

term reversals

Not all companies covered (e.g., few companies have

actively traded CDS or listed debt)

Illiquidity in bond and CDS markets reduce price

informative-ness

Bond yields are affected by interest rate movements that

are not related to default risk

Emerging markets may not have actively traded bonds or CDS

Market Derived Signals are represented in lowercase nomenclature to differentiate them from S&P Credit Ratings.


27/30


Peer Group Model

Fundamentally driven models offer a mid-term to long-term view of the

credit worthiness of entities.

They can be used for the following:

As inputs into longer-lasting strategic decision such as limit setting

For credit risk origination/underwriting policies Counterparty credit risk management

For debt pricing (fixed income, syndicated loans, transfer pricing etc.)

Fundamentals-Based

Credit Scoring or

PD Model

Issuer Credit

Ratings

When To Use Fundamental Measures Of Credit Risk


28/30


Global coverage includes 246 countries including emerging and frontier markets.

Market Derived Signals are represented in lowercase nomenclature to differentiate them from S&P Credit Ratings.

Market Signals-

Based PD Models

CDS Spreads

Market Signals-

Based PD Models

Stock Price Vola

Market signals of credit risk provide a short-term or point-in-time view

of the creditworthiness of entities.

They can be used for the following:

As inputs into early warning signals

As leading indicators of possible long-term credit quality shifts To monitor counterparty credit risk

To inform tactical or short-term credit related and investment management

decisions

When To Use Market Signals Of Credit Risk

Market Signals-

Based PD Models

Bond Spreads


29/30


Different Types Of Credit Risk Workflows:

Generating early-

warning indicators

Adjusting reserves

Calculating Value at

Risk (VaR)

Screening and simple risk

assessment

Benchmarking

Using models to score

companies

Stress-testing

Performing sensitivity

analysis

Origination/ Idea

Generation

In-Depth AnalysisSurveillance and

Monitoring

Credit Decision Accept or reject

exposure

Managing high-risk

entities: Adjust exposure terms

(less amount/ higher rate)

Outsource the risk, e.g.

insurance cover

Terminate exposure

Incorporating

entities into a

portfolio


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