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Irwin/McGraw-Hill 1 Credit Risk: Individual Loan Risk Chapter 11
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Irwin/McGraw-Hill 1 Credit Risk: Individual Loan Risk Chapter 11

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  • 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:
      • C&I loans: secured and unsecured
      • Spot loans, Loan commitments
      • Decline in C&I 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.
  • 4. 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)
  • 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.
  • 7. Credit Scoring Models:
      • Linear probability models: Z = XB + residuals. 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.
      • Logit models: overcome this weakness using a transformation (logistic function).
        • Other alternatives include Probit and other variants with nonlinear indicator functions.
  • 8. Altman’s Linear Discriminant Model:
      • Z=1.2X 1 + 1.4X 2 +3.3X 3 + 0.6X 4 + 1.0X 5
      • Critical value of Z = 1.81.
      • X 1 = Working capital/total assets.
      • X 2 = Retained earnings/total assets.
      • X 3 = EBIT/total assets.
      • X 4 = Market value equity/ book value LT debt.
      • X 5 = Sales/total assets.
  • 9. 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.
  • 10. 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.
  • 11. 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:
      • MMR 1 = (Value Grade B default in year 1) (Value Grade B outstanding yr.1)
      • MMR 2 = (Value Grade B default in year 2) (Value Grade B outstanding yr.2)
  • 12. 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:
      •  L = -D L x L 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/  L
  • 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.
  • 14. Applying Option Valuation Model
    • Merton showed value of a risky loan
    • F(  ) = Be -i  [(1/d)N(h 1 ) +N(h 2 )]
    • Written as a yield spread
    • k(  ) - i = (-1/  ) ln [N(h 2 ) +(1/d)N(h 1 )]
    • where k(  ) = Required yield on risky debt
    • ln = Natural logarithm
    • i = Risk-free rate on debt of equivalent maturity.
  • 15. *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.
  • 16. * 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.