Chapter 11
Credit Risk:
Individual Loan
Risk
K. R. Stanton
© 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.
Overview

11-2
This chapter discusses types of loans, and
the analysis and measurement of credit risk
on individual loans. This is important for
purposes of:


Pricing loans and bonds
Setting limits on credit risk exposure
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Credit Quality Problems
11-3
Problems with junk bonds, LDC loans,
residential and farm mortgage loans.
 More recently, credit card and auto loans.
 Crises in Asian countries such as Korea,
Indonesia, Thailand, and Malaysia.
 Default of one major borrower can have
significant impact on value and reputation of
many FIs
 Emphasizes importance of managing credit
risk

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Web Resources

11-4
For further information on credit ratings visit:
Moody’s www.moodys.com
Standard & Poors
www.standardandpoors.com
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Credit Quality Problems
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11-5
Over the early to mid 1990s, improvements in
NPLs for large banks and overall credit quality.
Late 1990s concern over growth in low quality
auto loans and credit cards, decline in quality of
lending standards.
Exposure to Enron.
Late 1990s and early 2000s: telecom companies,
tech companies, Argentina, Brazil, Russia, South
Korea
New types of credit risk related to loan guarantees
and off-balance-sheet activities.
Increased emphasis on credit risk evaluation.
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Types of Loans:

C&I loans: secured and unsecured
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11-6
Syndication
Spot loans, Loan commitments
Decline in C&I loans originated by commercial
banks and growth in commercial paper market.
Downgrades of Ford, General Motors and Tyco
RE loans: primarily mortgages


Fixed-rate, ARM
Mortgages can be subject to default risk when
loan-to-value declines.
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Consumer loans

11-7
Individual (consumer) loans: personal,
auto, credit card.

Nonrevolving loans

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Growth in credit card debt
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Automobile, mobile home, personal loans
Visa, MasterCard
Proprietary cards such as Sears, AT&T
Risks affected by competitive conditions and
usury ceilings
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Other loans

11-8
Other loans include:
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Farm loans
Other banks
Nonbank FIs
Broker margin loans
Foreign banks and sovereign governments
State and local governments
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Return on a Loan:
11-9
Factors: interest payments, fees, credit risk
premium, collateral, other requirements
such as compensating balances and
reserve requirements.
 Return = inflow/outflow
k = (f + (L + M ))/(1-[b(1-R)])
 Expected return: E(r) = p(1+k)-1 where p
equals probability of repayment
 Note that realized and expected return may
not be equal.

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Lending Rates and Rationing

At retail: Usually a simple accept/reject
decision rather than adjustments to the rate.
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
11-10
Credit rationing.
If accepted, customers sorted by loan quantity.
For mortgages, discrimination via loan to value
rather than adjusting rates
At wholesale:

Use both quantity and pricing adjustments.
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Measuring Credit Risk

11-11
Availability, quality and cost of information
are critical factors in credit risk assessment

Facilitated by technology and information
Qualitative models: borrower specific factors
are considered as well as market or
systematic factors.
 Specific factors include: reputation,
leverage, volatility of earnings, covenants
and collateral.
 Market specific factors include: business
cycle and interest rate levels.

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Credit Scoring Models

11-12
Linear probability models:
n
Zi =


  j X i, j  error
j 1
Statistically unsound since the Z’s obtained are
not probabilities at all.
*Since superior statistical techniques are readily
available, little justification for employing linear
probability models.
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Other Credit Scoring Models
11-13
Logit models: overcome weakness of the
linear probability models using a
transformation (logistic function) that
restricts the probabilities to the zero-one
interval.
 Other alternatives include Probit and other
variants with nonlinear indicator functions.
 Quality of credit scoring models has
improved providing positive impact on
controlling write-offs and default

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Altman’s Linear Discriminant Model:

11-14
Z=1.2X1+ 1.4X2 +3.3X3 + 0.6X4 + 1.0X5
Critical value of Z = 1.81.
 X1 = Working capital/total assets.
 X2 = Retained earnings/total assets.
 X3 = EBIT/total assets.

X4 = Market value equity/ book value LT debt.

X5 = Sales/total assets.
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Linear Discriminant Model

11-15
Problems:
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Only considers two extreme cases (default/no
default).
Weights need not be stationary over time.
Ignores hard to quantify factors including
business cycle effects.
Database of defaulted loans is not available to
benchmark the model.
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Term Structure Based Methods

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
11-16
If we know the risk premium we can infer the
probability of default. Expected return equals
risk free rate after accounting for probability of
default.
p (1+ k) = 1+ i
May be generalized to loans with any maturity
or to adjust for varying default recovery rates.
The loan can be assessed using the inferred
probabilities from comparable quality bonds.
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Mortality Rate Models


11-17
Similar to the process employed by insurance
companies to price policies. The probability of
default is estimated from past data on defaults.
Marginal Mortality Rates:
MMR1 = (Value Grade B default in year 1)
(Value Grade B outstanding yr.1)
MMR2 = (Value Grade B default in year 2)
(Value Grade B outstanding yr.2)
 Many of the problems associated with credit
scoring models such as sensitivity to the period
chosen to calculate the MMRs
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RAROC Models
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11-18
Risk adjusted return on capital. This is one of
the more widely used models.
Incorporates duration approach to estimate
worst case loss in value of the loan:
DLN = -DLN x LN x (DR/(1+R)) where DR is an
estimate of the worst change in credit risk
premiums for the loan class over the past year.
RAROC = one-year income on loan/DLN
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Option Models:
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11-19
Employ option pricing methods to evaluate the
option to default.
Used by many of the largest banks to monitor
credit risk.
KMV Corporation markets this model quite
widely.
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Applying Option Valuation Model
11-20
Merton showed value of a risky loan
F(t) = Be-it[(1/d)N(h1) +N(h2)]
 Written as a yield spread
k(t) - i = (-1/t)ln[N(h2) +(1/d)N(h1)]
where k(t) = Required yield on risky debt
ln = Natural logarithm
i = Risk-free rate on debt of equivalent
maturity.
t  remaining time to maturity

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*CreditMetrics
11-21
“If next year is a bad year, how much will I
lose on my loans and loan portfolio?”
VAR = P × 1.65 × s
 Neither P, nor s observed.
Calculated using:


(i)Data on borrower’s credit rating; (ii) Rating
transition matrix; (iii) Recovery rates on
defaulted loans; (iv) Yield spreads.
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* Credit Risk+

11-22
Developed by Credit Suisse Financial
Products.

Based on insurance literature:
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Losses reflect frequency of event and severity of
loss.
Loan default is random.
Loan default probabilities are independent.
Appropriate for large portfolios of small
loans.
 Modeled by a Poisson distribution.

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Pertinent Websites
11-23
Federal Reserve Bank
www.federalreserve.gov
OCC www.occ.treas.gov
KMV www.kmv.com
Card Source One www.cardsourceone.com
FDIC www.fdic.gov
Robert Morris Assoc. www.rmahq.org
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Pertinent Websites
11-24
Fed. Reserve Bank St. Louis www.stls.frb.org
Federal Housing Finance Board
www.fhfb.gov
Moody’s www.moodys.com
Standard & Poors
www.standardandpoors.com
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