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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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Todays Speakers
Marcel Heinrichs
Director, Business Development
S&P Credit Solutions
S&P Capital IQ
Mark Williams
Executive-in-Residence/Master Lecturer
Finance Department
Boston University School of Management
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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
Boston University School of Management
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
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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
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26 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.
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.
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27 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.
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
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28 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.
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
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29 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.
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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