Credit Risk: Individual Loan Risk Chapter 11

1
Credit Risk Individual Loan Risk Chapter 11

• Financial Institutions Management, 3/e
• By Anthony Saunders

2
Evaluation of Credit Risk

• Popular press attention to junk bonds and LDC
  loans. More recently, credit card loans and auto
  loans.
• In mid-90s, improvements in NPLs for large banks.
  
• New types of credit risk related to loan
  guarantees and off-balance-sheet activities.
• Increased emphasis on credit risk evaluation.

3
Types of Loans

• CI loans secured and unsecured
• Spot loans, Loan commitments
• Decline in CI loans originated by commercial
  banks.
• RE loans primarily mortgages
• mortgages can be subject to default risk when
  loan-to-value declines.
• Individual (consumer) loans personal, auto,
  credit card.

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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(1k)

5
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.
• At wholesale
• Use both quantity and pricing adjustments.

6
Measuring Credit Risk

• 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 Z XB residuals.
  Statistically unsound since the Zs obtained are
  not probabilities at all.
• Since superior statistical techniques are
  readily available, little justification for
  employing linear probability models.
• Logit models overcome this weakness using a
  transformation (logistic function).
• Other alternatives include Probit and other
  variants with nonlinear indicator functions.

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Altmans Linear Discriminant Model

• Z1.2X1 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)

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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
• DL -DL x L x (DR/(1R)) 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/DL

13
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(t) Be-it(1/d)N(h1) N(h2)
• Written as a yield spread
• k(t) - i (-1/t)lnN(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.

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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 s
• Neither P, nor s observed.
• Calculated using
• (i)Data on borrowers 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.