1. Credit Risk: Individual Loan Risk Chapter 11 © 2008 The McGraw-Hill Companies, Inc., All Rights Reserved. McGraw-Hill/Irwin

2. 11-2 Overview  This chapter discusses types of loans, and 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. 11-3 Credit Quality Problems  Problems with junk bonds, LDC loans, residential and farm mortgage loans.  More recently, credit card and auto loans.  Crises in Asian countries such as Korea, Indonesia, Thailand, and Malaysia.  Default of one major borrower can have significant impact on value and reputation of many FIs  Emphasizes importance of managing credit risk

4. 11-4 Web Resources  For further information on credit ratings visit: Moody’s www.moodys.comwww.moodys.com Standard & Poors www.standardandpoors.com www.standardandpoors.com

5. 11-5 Credit Quality Problems  Over the early to mid 1990s, improvements in NPLs for large banks and overall credit quality.  Late 1990s concern over growth in low quality auto loans and credit cards, decline in quality of lending standards.  Exposure to Enron.  Late 1990s and early 2000s: telecom companies, tech companies, Argentina, Brazil, Russia, South Korea  Mid 2000s, economic growth accompanied by reduction in NPLs  New types of credit risk related to loan guarantees and off-balance-sheet activities.  Increased emphasis on credit risk evaluation.

6. 11-6 Loan Growth and Asset Quality

7. 11-7 Types of Loans:  C&I loans: secured and unsecured Syndication Spot loans, Loan commitments Decline in C&I loans originated by commercial banks and growth in commercial paper market. Downgrades of Ford, General Motors and Tyco  RE loans: primarily mortgages Fixed-rate, ARM Mortgages can be subject to default risk when loan-to-value declines.

8. 11-8 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 Consolidation among credit card issuers  Bank of America & MBNA Risks affected by competitive conditions and usury ceilings Bankruptcy Reform Act of 2005

9. 11-9 Annual Net Charge-Off Rates on Loans

10. 11-10 Other loans  Other loans include: Farm loans Other banks Nonbank FIs Broker margin loans Foreign banks and sovereign governments State and local governments

11. 11-11 Return on a Loan:  Factors: interest payments, fees, credit risk premium, collateral, other requirements such as compensating balances and reserve requirements.  Return = inflow/outflow 1+k = 1+(of + (BR + m ))/(1-[b(1-RR)])  Expected return: E(r) = p(1+k) - 1 where p equals probability of repayment  Note that realized and expected return may not be equal.

12. 11-12 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. For mortgages, discrimination via loan to value rather than adjusting rates  At wholesale: Use both quantity and pricing adjustments.

13. 11-13 Measuring Credit Risk  Availability, quality and cost of information are critical factors in credit risk assessment Facilitated by technology and information  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.

14. 11-14 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.

15. 11-15 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.  Quality of credit scoring models has improved providing positive impact on controlling write-offs and default

16. 11-16 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.

17. 11-17 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.

18. 11-18 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.

19. 11-19 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) Many of the problems associated with credit scoring models such as sensitivity to the period chosen to calculate the MMRs

20. 11-20 RAROC Models Risk adjusted return on capital. This is one of the most widely used models. RAROC = (one year net income on loan)/(loan risk) Loan risk estimated from loan default rates, or using duration.

21. 11-21 Using Duration to Estimate Loan Risk For denominator of RAROC, duration approach used to estimate worst case loss in value of the loan:  LN = -D LN x LN 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/  LN

22. 11-22 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.

23. 11-23 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.  remaining time to maturity

24. 11-24 * 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.

25. 11-25 Pertinent Websites Federal Reserve Bank www.federalreserve.gov www.federalreserve.gov OCC www.occ.treas.govwww.occ.treas.gov KMV www.kmv.comwww.kmv.com eCID www.cardindustrydirectory.comwww.cardindustrydirectory.com FDIC www.fdic.govwww.fdic.gov Robert Morris Assoc. www.rmahq.orgwww.rmahq.org

26. 11-26 Pertinent Websites Fed. Reserve Bank St. Louis www.stls.frb.orgwww.stls.frb.org Federal Housing Finance Board www.fhfb.gov www.fhfb.gov Moody’s www.moodys.comwww.moodys.com Standard & Poors www.standardandpoors.com www.standardandpoors.com