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Credit Portfolio Management
Current State of Practice
Charles Smithson
Managing Partner, Rutter Associates
Copyright 2001 Rutter Associates, LLC
www.rutterassociates.com
Credit Markets Are Being Transformed
The way credit risk is measured and managed is
changing, because of
• Market realities
• Changing regulatory environment
• Theoretical and analytical advancements
• Technology
• Advent of new measurement/management tools
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Market Realities
• Higher risks -- More concentration in loan
portfolios (to less creditworthy obligors)
• Lower return
• Banks are no longer the primary holder of loans
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Defaults in Excess of $1 Billion
1998
Home Holdings
Boston Chicken
Criimi Mae
Grand Union
Mercury Finance
1999
Loewen Group
Iridium
Harnischfeger
ICO Global
Sun Healthcare
SOURCE: Barry L. Campbell, BMO Nesbitt Burns,
“Investing in Assets with Credit Risk,” RISK Credit
Summit, September 28, 2000
2000
AMF Bowling
Armstrong World Ind
Genesis Health Ventures
ICG Communications
Laidlaw
Owens Corning
Paging Network
Pathmark Stores
Pillowtex
Safety-Kleen
SOURCE: “Corporate Defaults: Will Things
Get Worse Before They Get Better?” S&P
CreditWeek, Jan 31, 2001
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Question posed to Loan ORIGINATORS:
What is the bank’s perception regarding large corporate and middle
market loans?
a) Loans generate sufficient profit that they add shareholder value
b) Loans do not add shareholder value by themselves; they are used
as a way of establishing or maintaining a relationship with the
client … but the loan product must be priced to produce a
positive NPV.
c) Loans do not add shareholder value by themselves; they are used
as as way of establishing or maintaining a relationship with the
client … and the loan product can be priced as a “loss leader.”
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Question posed to Loan ORIGINATORS:
What is the average ROE for…
…revolving & backup facilities
for large, investment-grade
companies?
…term loans to middle-market
growth (below investment grade)
Companies?
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Question posed to Loan ORIGINATORS:
For non-investment grade loans that your bank originates…
…what is the maximum “hold level”?
…what is the target “hold level”?
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Primary Market for Highly Leveraged Loans by Broad Investor Type 1994 – LTM6/30/2000
50%
Share of Market
40%
Institutional Investors
Foreign Banks
30%
Domestic Banks
20%
10%
0%
1994
1995
1996
1997
1998
1999
LTM6/30/00
Source: S&P/Portfolio Management Data
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Providers of Software or Data for Probability of Default
Vendor
Altman
Kamakura
KMV
LPC
Moody’s
S&P
Product Name
Risk Mgr – Credit Risk Suite
KRIS – cr,cs
Private firm model under construction
Credit Monitor
EDF Calc
Private Firm Model
Risk Rater
Historical Transition Matrices
Risk Calc for Public Companies
Risk Calc for Private Companies
CreditPro
CreditModel
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Question posed to Loan INVESTORS (PORTFOLIO MANGERS)
What is the primary source of probabilities of default for your
portfolio model?
a) Bond Migration studies (e.g, Moody’s, S&P, Altman)
b)
KMV’s EDFs
c) Probabilities of default implied from term structures of
spread
d)
Internal review of portfolio migration
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Question posed to Loan INVESTORS (PORTFOLIO MANGERS)
What is the primary source of data on Loss in the Event of Default
for your portfolio model?
a) Internal review of charge-offs
b) Third-party data bases
c) Industry-wide studies
d) Rating agency analysis
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Providers of Data on Utilization
Vendor
Product Name
LPC
Loan Loss Database
S&P
CreditPro
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Black-Scholes-Merton
No Arbitrage/Contingent Claims Analysis
“Structural Models”
“Reduced Form Models”
First Generation Models
Third Generation Models
Merton (1974)
Black and Cox (1976)
Jarrow and Turnbull (1995)
Madan-Unal (1996)
Duffie and Singleton (1998)
Jarrow Lando and Turnbull (1995)
Das and Tufano (1996)
Second Generation Models
Hull and White (1995)
Longstaff and Schwartz (1995)
“Fourth” Generation Models
Risk Management Models
Madan and Unal (1999)
Creditmetrics™ and KMV
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Question posed to Loan INVESTORS (PORTFOLIO MANGERS)
Do you use a credit portfolio model?
Obligor
Facility
Exp
Loss
Portfolio
Unexp
Loss
Capital
Risk
Cont
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Providers of Credit Portfolio Models
Developer/Vendor
Product Name
CSFB
CreditRisk+
KMV
Portfolo Manager
Kamakura
Risk Manager – Credit Risk Suite
McKinsey
CreditPortfolioView
RMG
Credit Manager
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Question posed to Loan INVESTORS (PORTFOLIO MANGERS)
Which of the following is your primary credit portfolio model?
a) CreditMetrics
b) CreditRisk+
c) KMV’s Portfolio Manager
d) CreditPortfolioView
e) Internally-developed model
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Macro Factor CreditRisk+
Portfolio Manager CreditManager
CHARACTERISTICS
Model Type
Structural
Structural
Factor
Actuarial
Asset Value
Asset Value
Macro Factors
Default Event
Both
MTM
MTM
Default Only
Endogenous
Exogenous
Exogenous
Exogenous
Exogenous
Exogenous
Exogenous
Exogenous
Asset Correlation
Equity Correlation
Endogenous
Endogenous
Loss Distribution
Simulation
Simulation
Simulation
Gamma
Valuation Model
Quasi EDF
Forward Spread
Forward Spread
N.A.
Stochastic Variable
MTM or Default?
INPUTS
Probability of Default
LGD
Default Correlation
CALCULATION
Switches to Set
Spread
Recovery
Recovery Distribution Treatment of Commitments
Treatment of Commitments Iterations in Simulations
FX & Interest rates
Pricing parameters
Xxxx
Default Rate Volatility
OUTPUT
Risk Contribution
Capital
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Standard Dev
Mar Std Dev
Incr Std Dev
Multiple of Risk Cont Multiple of Risk Cont
Standard Dev
ETA
Standard Dev
Direct Estimate Multiple of Risk Cont
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Existing Model Comparisons
• Descriptive
– Basle Committee on Banking Supervision (1999)
– Federal Reserve System Task Force (1998)
• Mathematical
– Gordy (2000)
– Lopez and Saidenberg (2000)
– Hickman and Koyluoglu (1998)
• Empirical
• ISDA/IIF
• Occasional ad hoc comparisons using available data sets
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Existing Model Comparisons (Continued)
• Academic
– Identify mathematical relation between model types
– Some simulation over stylized portfolios
– Mathematically and experimentally models can give similar results
• ISDA/IIF
– Ambitious range of portfolio types and model types (25 Banks
participated with a variety of models and portfolios )
– Similar inputs result in similar outcomes
– One time effort; not reproducible
• Industry
– Use of “actual” portfolios to illustrate use of the model
– Focus on applications: Hedging; Capital allocation
– Reproducible results
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Portfolio Selection Process
KMV
Non-Fin Universe
7287 N.A. Firms
Random sample of
Approx. 3000
Non-Fin obligors
KMV
Financial Universe
1722 N.A. Firms
Proper subset of
KMV
Non-Financial
Modify Selection
To Obtain Desired
Rating Distribution
Proper subset of
KMV
Financial
Non-Financial
Test portfolio
Financial
Test portfolio
Filtered by
Geography, Industry,
Facility, etc.
Starting Portfolio
2903 Obligors
3136 Exposures
N.A.  North America. Asian and European portfolios are also available.
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S&P Rating
(Implied)
Distribution of
Obligors
AA
A
BBB
BB
B
CCC
Grand Total
Investment Grade
Non-Investment Grade
Distribution of
Commitments
2.2%
6.5%
34.2%
30.6%
17.6%
9.0%
100.0%
7.4%
11.0%
38.2%
27.6%
10.6%
5.1%
100.0%
42.9%
57.1%
56.6%
43.4%
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Distribution of Commitments by Facility Type
Term Loan
Guarantee
Bonds
Grand Total
Facility Share
55%
13%
11%
20%
100%
AA
10%
0%
9%
5%
7%
A
12%
0%
17%
13%
11%
BBB
40%
13%
53%
43%
38%
BB
27%
50%
10%
25%
28%
B
8%
25%
8%
10%
11%
CCC
4%
13%
4%
5%
5%
100%
100%
100%
100%
100%
Rating Composition
Revolvers
Notes: Very few firms have an implied AAA rating based on KMV EDF values
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Models Used
KMV
Portfolio Manager
RiskMetrics Group
CreditManager
CSFB
CreditRisk+
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Macro Factor Model*
*The Macro Factor Model implements the approach described by Wilson in CreditPortfolioView
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Typical Implementation of Risk Contribution
Standard deviation
Percentile for Capital
`
Expected Loss
99.9th
Percentile
Risk Contribution
Standard Deviation
Is Allocated to Deals
Allocations Sum to
Portfolio Standard Deviation
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Typical Implementation of Risk Contribution
sp
RCAsset = xA
 xA
By construction risk contributions sum
to portfolio standard deviation
SA RCAsset = sp
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Typical Capital Allocation
Capital is a multiple of Risk Contribution
CapAsset = RCAsset x
Portfolio Capital
sp
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Alternative Risk Contribution Measures
• Standard deviation based measure -- contribution
per dollar of exposure (KMV, RMG, CR+, MF)
• Standard deviation based measure -- contribution
per deal (currently RMG)
• Marginal contribution (measured at a given
percentile loss)
– Incremental effect of a transaction on a given
percentile of the distribution
– Marginal contributions do not sum to standard
deviation or to capital
• Capital-based measure
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An Alternative Capital-Based Risk Contribution
“Empirical Tail Allocation (ETA)”
Standard deviation
ETA models losses in
the tail region
`
98th
<100
99.9th
Percentile
Expected Loss
Portfolio Losses
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ETA Concept
• Simulate a large number of portfolio losses
– Identify losses in the “neighborhood” of capital
• For each loss in the neighborhood of capital
– Identify those exposures that contributed to the loss
– Identify how much each exposure lossed (optional)
• Calculate conditional probabilities
– Conditional on a capital-level loss
• Probability of Obligor “A” contributing
• Expected dollar amount of “A’s” contribution
• Conditional probabilities and losses are scaled to allocate
economic capital or calculate risk contribution
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Question posed to Loan INVESTORS (PORTFOLIO MANGERS)
Rank the following tools in order of their importance to the management of your credit portfolio.
(“1” denotes the most important and “4” denotes the least important.)
Management of new business and renewals of existing business
Loan sales and trading
Credit derivatives
Securitizations
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To meet the challenge of increasing riskadjusted return ...
... the managers of credit portfolios must adopt
Modern Portfolio Management to create efficient
portfolios
... banks must become “asset managers” with respect
to their own portfolios
...the organization of financial institutions must
change
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Question posed to Loan INVESTORS (PORTFOLIO MANGERS)
Do you now or do you plan to mark the loan portfolio to market
or model?
a) Currently mark to market
b)
Currently mark to model
c) Plan to mark to market or model in the future
d)
No plans to mark to market or model
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Survey of Credit Market Participants
(December 2000 issue of CREDIT)
Who “owns” the economics of the loan portfolio?
a) Portfolio Management exclusively
b) Line Units exclusively
c) Portfolio Mgmt / Line partnership
d) Treasury
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