Palestra apresentada por Conrad Albrecht, head de Analytics para a América Latina da MSCI Inc

1. © 2017 MSCI Inc. All rights reserved.
Please refer to the disclaimer at the end of this document.
CREDIT RISK
MANAGEMENT
MSCI CREDITMETRICSTM CREDITANAGERTM
CONRAD ALBRECHT

2. • Credit Risk and Challenges
• CreditMetrics Methodology Overview
• Applications and use-cases
AGENDA
2

3. © 2015 MSCI Inc. All rights reserved.
Please refer to the disclaimer at the end of this document.
CREDIT RISK AND
CHALLENGES
3

4. • Traditional Credit Risk Assessment (policies):
• Individual and group credit ratings
• Credit risk limits (exposure-based)
• Obligor simple concentrations
• Credit Scorecards
• Counterparty monitoring
• Financial Ratios, Fundamental Analysis
• Cash-flows, credit quality, leverage ...
• Judment and experience of the analyst
CREDIT RISK AND CHALLENGES
4
Qualitative feel,
intuitive, but static
and arbitrary
Fixed exposure limits and other
related metrics do not recognize the
relationship between risk and return
• Industrial sectors
• Geographical location
• Product type
• Obligor groups ...
Subjective!

5. • Portfolio Approach – quantitative standpoint
• Foundation for rational risk-based capital allocation process
• Systematically address overconcentration risk
• State credit lines and limits in terms of marginal portfolio Volatility
• Assess opportunities for diversification
• Better identifiy pockets of concentration risk
CREDIT RISK AND CHALLENGES
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• Upgrades, downgrades
• Volatility of value – VaR
• Correlation of credit quality
moves across obligors
Dynamic risk assessment due to
changes in debt value caused by
changes in obligor credit quality

6. • Credit Markets
• Non-normal distributions
• Highly skewed and fat-tailed
• Log-normal and t-student are
not appropriate
• Lack of data and iliquid markets:
difficulty of modeling
correlations
• > More than standard statistics
are needed
CREDIT RISK AND CHALLENGES
6
Challenges
• Equity Markets
• Normal, Gaussian
distributions = symetric
returns
• Mean (average) and Std.
Deviation of portfolio value
are sufficient for a quick and
meaningful portfolio market
risk understainding
Long downside
caused by defaults
Credit returns characterized likelihoods:
earning small profits through net interest
while losing small amount of investment
Long tail on “loss” and limited “gains”

7. CREDIT RISK AND CHALLENGES
7
• Distribution values for a single bond
• BBB bond, maturing in 5 years, face value of $100, 6% coupon
• Analysis, risk horizon: one year
...
Different likelihoods of credit rating migration (credit events)
(%) Based on
historical rating data
Recovery rateBBB rise in value due to
credit spread as curve is
upward sloping

8. CREDIT RISK AND CHALLENGES
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• Distribution values for two-bonds (portfolio)
• BBB bond, maturing in 5 years, face value of $100, 6% coupon
• Analysis, risk horizon: one year
+
• A bond, maturing in 3 years, face value of $100, 5% coupon

9. CREDIT RISK AND CHALLENGES
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• The portfolio now grows > 2 or more...
• 3 bonds = 512 possible joint rating states
• 5 bonds = 32,768 ... Exponencial growth
• From a Accounting-Analytical approach to a Simulation approach!
• Resulting in a more smooth curve than a collection of few points
Two simple credit risk measures to measure and quantify portfolio value
distribution:
Reflecting potential losses from the same portfolio distribution
Standard
Deviation
Percentile
Level

10. CREDIT RISK AND CHALLENGES
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• Standard Deviation
• The greater the dispersion (individual values), greater the risk
• Computationally simple and efficient
• Great for market risk, not so much for credit risk (non-normal)
• Percentile level
• Intuitive and simpler interpretation than Std.Dev
• Lowest value that portfolio will achieve 1% of the time = 1st perctile.
• Requires simulation approach for > 2 pos-portfolios (i.e. MC), takes more time to
compute
• More meaningful statistic for large portfolios
• Marginal Risk
• Idenfify (over)concentrations
• Promotes an idea of diversification (# stand-alone and marginal risk)
• Better picture of true concentration risk regarding a given counterparty

11. CREDIT RISK AND CHALLENGES
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• What else is needed?
• Compute expected but also non-expected losses (volatility)
• Estimate recovery and uncertainty on recovery rates
• Address default and credit quality, rating migration (transition matricies)
• Start to address credit quality statistically significant correlations
(diversification)
• Asset value models – spreads:
Risk free interest rates
Credit spreads
Independent movements

12. CREDIT RISK AND CHALLENGES
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• Asset value models – spreads
• Migration thresholds – factor model
• Comparison of simulated asset returns w/ rating thresholds
• Determine the future rating
Under normal market conditions, equity and asset returns are normally
distributed. Phi is the cumulative normal density of probability.

13. © 2015 MSCI Inc. All rights reserved.
Please refer to the disclaimer at the end of this document.
CREDITMETRICSTM
METHODOLOGY OVERVIEW
13

14. CREDITMETRICSTM METHODOLOGY
14
• CreditMetrics Risk Modeling
• Estimates portfolio risk due to risk events
• Uncertainty in the forward value of the portfolio risk horizon caused by
the possibility of obligor credit quality changes (up/downgrades)
• Captures market risk components such as volatility (longer horizon for
credit risk, different than market risk)
• Based on expected default likelihood of the value of the firm: Merton
• Hull-White pricing framework ($# risky vs riskless bonds = potentail loss
due to default) which includes recovery rates, collateral in non-default
and default valuations
• Focus on risk assessment rather than focus on the pricing side
• Provides stress testing on probabilities of default, correlations, spreads
and recovery rates
• Promotes risk-mitigating actions
Risky price = Risk free price – PV of Expected Loss

15. CREDITMETRICSTM METHODOLOGY
15
• CreditMetrics is the credit risk methodology underlying CreditManager
• Structural framework where asset returns are simulated and may cross
thresholds for downgrades or defaults, in a correlated manner
• General ‘goal’ is to generate horizon value distribution for holdings,
from which a variety of risk statistics can be gleaned

16. CREDITMETRICSTM METHODOLOGY
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DefaultProbability
Marketfactor
correlation
RatingMigration
‘Book Value’ mode, no valuation
Default Probability and Correlation drive risk
‘Market Value’ mode, valuation + revaluation at horizon
Default Probability, Rating Migration, and Correlation drive risk
• Drivers of risk in Creditmetrics CreditManager

17. CREDITMETRICSTM METHODOLOGY
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• Datasources for risk in Creditmetrics CreditManager
DefaultProbability
Marketfactor
correlation
RatingMigration
MSCI Equity Indexes
Custom indexes
Through the cycle
transition matrices (S&P,
Moody’s)
Client internal PDs
Transition matrices
(S&P, Moody’s)
Spread matrices, rating
by term structure,
across sectors
Client matrices, spreads

18. CREDITMETRICSTM METHODOLOGY
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• R² as a Valve
• R² works to decompose obligor correlation in to a market and
idiosyncratic component
• R² determine how much of the obligor’s movements are actually
captured by common factors
• As R² approaches 1, market factors describe more and more of the
correlation between obligors
• As R² approaches 0, the idiosyncratic component moves in, and
obligors move toward independence
R2 acts like a valve
the higher values allow more correlation to ‘flow’
from index returns correlations to asset correlation

19. CREDITMETRICSTM METHODOLOGY
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• CreditMetrics Transition Matrices < > Migration Analysis
• Combines the likelihoods from the BBB and A bond samples
• Computed on historical pattern of rating change and default given a risk
horizon
• Probabilistic method: Long term estimation rather than recent
observation
• Used to calculate volatility of values due to credit quality
changes rather than just expected losses (unexpected loss)

20. CREDITMETRICSTM METHODOLOGY
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• Stand-alone risk calculation
• Lack of quality and availability of data = we construct what we cannot
directly observe
Step 1: Risk Migration
Step 2: Valuation
Step 3: Credit Risk Estimation
= volatility of value due to credit quality changes

21. CREDITMETRICSTM METHODOLOGY
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Stand-alone risk calculation
• Step 1: Rating Migration
• Defaults and up(down)grades
• Transition matrices

22. CREDITMETRICSTM METHODOLOGY
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Stand-alone risk calculation
• Step 2: Valuation
• Valuation -default
• Valuation -up(down)grade

23. CREDITMETRICSTM METHODOLOGY
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Stand-alone risk calculation
• Step 3: Credit Risk Estimation
• Now, estimate vol due to credit
quality changes
1st Percentile

24. CREDITMETRICSTM METHODOLOGY
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• Portfolio risk calculation
• Now, estimates the contribution to risk brought by the effects of non-
zero credit quality correlations
Lower the VaR to Credit as lower the correlation of credit events

25. CREDITMETRICSTM METHODOLOGY
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Portfolio risk calculation
• Joint Probabilities
Zero Correlation: to simplistic, unrealistic
Default as a function to the underlying and
volatile value of the firm: Merton Model or
option theoretic valuation of debt (based
on B&S option pricing model)
B&S
Firm risk
component
Put option
firm assets

26. CREDITMETRICSTM METHODOLOGY
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Firm’s asset value relative to
these thresholds determines its
future rating
Portfolio risk calculation
• Joint Probabilities
The model includes rating
changes and rating and
default thresholds

27. CREDITMETRICSTM METHODOLOGY
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• Portfolio risk calculation
• Finally, addresses different, a variety of exposure types
Receivables, bonds, loans, loan commitments, financial letters, market-driven instruments...
Amount subject to changes in value upon credit up(down)grade or loss in default

28. © 2015 MSCI Inc. All rights reserved.
Please refer to the disclaimer at the end of this document.
APPLICATIONS AND
USE-CASES
28

29. APPLICATIONS
29
• Recall the CM Framework
(Re)Compute
Capital
Retrieve
PD, LGD, Correlation
From Risk Relevant
scenarios
Consult empirical
data, forecasts
and views for
these risk factors
Adjust risk factors

30. APPLICATIONS
30
• MC Simulation in CM
• Generate scenarios:
• Value portfolio: new credit ratings
• Summarize results: estimative of the new distribution
• Establish asset returns > generate scenarios > map with rating scenarios

31. 31
• MC Simulation in CM with a larger portfolio
• 20 corporate bond positions
• Portfolio PV of $ 68mm
• + Asset correlations (portfolio)
APPLICATIONS

32. APPLICATIONS
32
• MC Simulation in CM with a larger portfolio
• 20,000 scenarios! Simulation Results of future portfolio values
• Scenarios with odd bimodal behaviour (defaults prevails on rtg migrations)
Most common
Left tail
Most 5% extreme
2~3 issuer defaults
1 issuer
defaults
no rating
change

33. APPLICATIONS
• MC Simulation in CM with a larger portfolio
• Marginal risk of value change
• Ploting m.Stdev x Mkt.Value
# is the diversification
Risky assets, but low
exposure size
Less risky, higher
exposure size
33

34. APPLICATIONS
• Set priorities for actions to reduce risk
• Measure and compare risks, limiting risk over-concentrations
• Economic capital estimation (risk-taking)
• Direct actions
• Utilize risk-taking capacity more efficiently
• Optimize the return given the risk taken
34

35. APPLICATIONS
• Reduce Risk by...
• Absolute exposure size
• Statistical risk level
35
Highest volatilities
“Fallen angels” ... Credit
quality movement

36. APPLICATIONS
• Reduce Risk by...
• Credit Limit setting
• What type of limits?
• Which risk measure to use?
• What policy to employ?
...
• Risk vs Return
36
% Risk?
(=credit quality)
$ exposure size?
Absolute
Risk? (Cap)

37. APPLICATIONS
• Economic Capital Assessment
• The capital that the firm puts in risk by holding a credit portfolio
• Risk in terms of the stability of the organization, in terms of capital
• Not accounting or from a regulatory perspective
• Firm’s capital, liabilities being constant – taking risk that is volatile
• Assets volatility could drop firm’s capital value and thus its liability obligations
• Which statistic to use?
• Std.Dev? No!
• Percentiles: like the 1st, we will know the chances of losses at 99%
• Expected (average) shortfall: expected given loss in addition to the percentile
37
Assets vs Liabilities ALM

38. APPLICATIONS
38
Notional Rating Recovery mean Loss Given Default Recovery standard
deviation
Correlation
$2bn B+ to BB+ 70% 30% 20% 30%
• Suppose CreditManager computes 99% VaR at
200m, or 10%
• Is this ‘right’?
• How does this line up with history?
• What does this imply for future? Recovery distribution
Transition matrix
Loan Portfolio Sample Review

39. APPLICATIONS
39
Simulated values retrieved from tail (risk
relevant scenarios)Through the cycle
• CreditManager Output

40. APPLICATIONS
40
• CreditManager Output
Simulated values retrieved from tail (risk
relevant scenarios)Through the cycle
Recovery rate distribution
Conditional recovery rates or LGD (1 – Recovery)

41. APPLICATIONS
41
• Recall...
PD
LGD
CORRELATION
PD
LGD
CORRELATION
Simulation
Through the cycle parameters
provided by risk analyst
Conditional values simulated
by CreditManager, which are
used for capital

42. USE CASES – NORTH AMERICA, EMEA AND ASIA
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Banks
Loan Book
Asset
Managers
Asset
Owners
and
Insurance
Banks
Trading
Book
Hedge
Funds
Basel Pillar II,
Economic Capital
Basel Pillar I,
Regulatory Capital
(IRB formula =
simulation base
framework)
Stress testing and
scenario analysis
Correlated
Recovery
Basel II.5,
Incremental Risk
Charge (IRC)
Basel III,
Incremental
Default Risk
Charge (IDRC)
Stress testing
and scenario
analysis
Risk Attribution
Module
Solvency II
Credit risk
reporting
Stress testing and
scenario analysis
More hybrid use
case: regulatory
and credit risk
reporting:
concerned about
long term horizon,
mark risk, interest
rate and market
spread volatility
… and “is my
money good?”, “is
my money good in
the long term
horizon?”
Credit risk
measurement for
long term (3-5y)
credit strategy
allocation (buy
and hold)…
and use CM for
understanding
migration to
default
Stress testing and
scenario analysis
Risk reporting,
loan fund credit
strategy
Hedge Funds are
interested in
understanding
the Correlation
that is implied in
the banking
pricing model
They will use it
for correlation
trading
Central
Banks
Capital
calculation for
reserve portfolio
Collateralized
lending risk
reporting
Double default
captured in CM

43. USE CASES – NORTH AMERICA, EMEA AND ASIA
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44. USE CASES – NORTH AMERICA, EMEA AND ASIA
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45. © 2015 MSCI Inc. All rights reserved.
Please refer to the disclaimer at the end of this document.
EXTRAS
RISK ATTRIBUTION
CORRELATED RECOVERY
45

46. EXTRAS
46
• Advanced Modules in CreditManager
Risk
Attribution
Module
Correlated
Recovery

47. EXTRAS – ATTRIBUTION MODULE
47
• Understanding changes in capital across time is a fundamental requirement
• Various moving parts within CreditManager can make distilling capital changes a challenge
 The Attribution Module allows for a systematic approach for understanding changes
New exposures
Reduced exposures
Spread movements
Rating changes
FX
Yields curve movements
Correlation changes
Etc
Changes
Initial Capital Ending Capital

48. EXTRAS – ATTRIBUTION MODULE
48
• Factor changes from time (t-1) to time (t) realized individually, on a cumulative basis
• Ordering defined by user
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
Recomputed
Capital
• Each column above represents a full simulation of the portfolio

49. EXTRAS – ATTRIBUTION MODULE
49
This example focuses on two portfolio changes
• Recovery prospects on health care shift down 15% to 55%
• 200mm notional shifted from Cons Disc (B+) to Industrial (BB-)

50. EXTRAS – CORRELATED RECOVERY
50
• Correlated Recovery in CreditManager
─ Impact on risk numbers
• Evidence for Recovery Correlation
─ Empirical analysis based on historical data
─ Third party approaches, regulation
─ Balance sheet model
─ Point-in-time recovery correlation estimation without using historical data
• A must, or too much?
─ Myth 1: Stochastic recovery is enough if risk quantile is sufficiently high
─ Myth 2: The increase in total risk under Correlated Recovery is excessive
• Risk may increase by up to 40% for certain positions
─ Impact strongly depends on mean and std. of recovery rates
• Seniority plays important role for value of Recovery-R2
Bond Seniority Type Recovery R-Squared
Junior Subordinated 0% - 10%
Subordinated 5% - 15%
Senior Subordinated 10% - 25%
Senior Unsecured 20% - 35%
Senior Secured 10% - 25%

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