Credit Risk: Credit Risk: Individual Loan Individual Loan

RiskRisk

Chapter 11

© 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.

K. R. Stanton

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Overview

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

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

For further information on credit ratings visit:

Moody’s www.moodys.com

Standard & Poors www.standardandpoors.com

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Credit Quality Problems

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 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

Individual (consumer) loans: personal, auto, credit card. Nonrevolving loans

Automobile, mobile home, personal loans Growth in credit card debt

Visa, MasterCard Proprietary cards such as Sears, AT&T

Risks affected by competitive conditions and usury ceilings

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Other loans

Other loans include: 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:

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. 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

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

Linear probability models:

Zi =

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.

n

jjijX

1, error

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Other Credit Scoring Models

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:

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

Problems: 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

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

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

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:

LN = -DLN x LN x (R/(1+R)) where R 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/LN

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Option Models:

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

Merton showed value of a risky loan

F() = Be-i[(1/d)N(h1) +N(h2)] Written as a yield spread

k() - i = (-1/)ln[N(h2) +(1/d)N(h1)]

where k() = Required yield on risky debt

ln = Natural logarithm

i = Risk-free rate on debt of equivalent maturity.

remaining time to maturity

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*CreditMetrics

“If next year is a bad year, how much will I lose on my loans and loan portfolio?”

VAR = P × 1.65 × Neither P, nor 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+

Developed by Credit Suisse Financial Products. Based on insurance literature:

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

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

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