Irwin/McGraw-Hill 1 Credit Risk: Individual Loan Risk Chapter 11
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share

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

  • 3,059 views
Uploaded on

 

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
3,059
On Slideshare
3,058
From Embeds
1
Number of Embeds
1

Actions

Shares
Downloads
41
Comments
0
Likes
0

Embeds 1

http://www.slideshare.net 1

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 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.