Individual loan credit risk

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  • 1. BUS FINANCE 826
  • 2. Overview
    • 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
  • 3. Credit Quality Problems
    • Problems with junk bonds, LDC loans, residential and farm mortgage loans.
    • More recently, credit card loans and auto loans.
    • Crises in Asian countries such as Korea, Indonesia, Thailand, and Malaysia.
  • 4. Web Resources
    • For further information on credit ratings visit:
    • Moody’s www.moodys.com
    • Standard & Poors www.standardandpoors.com
    Web Surf
  • 5. Credit Quality Problems
    • Over the 90s, improvements in NPLs for large banks and overall credit quality.
    • Recent exposure to borrowers such as Enron.
    • New types of credit risk related to loan guarantees and off-balance-sheet activities.
    • Increased emphasis on credit risk evaluation.
  • 6. Types of Loans:
    • C&I loans: secured and unsecured
      • Spot loans, Loan commitments
      • Decline in C&I loans originated by commercial banks and growth in commercial paper market.
    • RE loans: primarily mortgages
      • Fixed-rate, ARM
      • Mortgages can be subject to default risk when loan-to-value declines.
  • 7. 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
  • 8. Other loans
    • Other loans include:
      • Farm loans
      • Other banks
      • Nonbank FIs
      • Broker margin loans
      • Foreign banks and sovereign governments
      • State and local governments
  • 9. 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)
  • 10. 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.
  • 11. 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.
  • 12. Credit Scoring Models
    • Linear probability models:
    • Z i =
      • 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.
  • 13. 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.
  • 14. 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.
  • 15. 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.
  • 16. 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.
  • 17. 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)
  • 18. 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
  • 19. 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.
  • 20. 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.
  • 21. *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.
  • 22. * 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.
  • 23. Pertinent Websites
    • For more information visit:
    • 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
    • Credit Metrics www.creditmetrics.com
    • Robert Morris Assoc. www.rmahq.org
    Web Surf
  • 24. Pertinent Websites
    • The Economist www.economist.com
    • Fed. Reserve Bank St. Louis www.stls.frb.gov
    • Federal Housing Finance Board www.fhfb.gov
    • Moody’s www.moodys.com
    • Standard & Poors www.standardandpoors.com
    Web Surf