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Credit Risk: Individual Loan Risk Chapter 11. Financial Institutions Management, 3/e By Anthony Saunders. 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.

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## Credit Risk: Individual Loan Risk Chapter 11

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**Credit Risk: Individual Loan Risk Chapter 11**Financial Institutions Management, 3/e By Anthony Saunders**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.**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.**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)**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.**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.**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.**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.**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.**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.**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)**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/(1+R)) 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**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.**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)ln[N(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.***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 borrower’s credit rating; (ii) Rating transition matrix; (iii) Recovery rates on defaulted loans; (iv) Yield spreads.*** 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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