Chapter 8 Credit Risk I: Individual Loan Risk

8-2 Overview This chapter introduces the key considerations involved in the credit assessment process. We discuss the risks involved in lending to individual borrowers. We examine the different types of loans issued by FIs and the characteristics of commercial and industrial loans. We learn about the nature and risks of real estate loans and consumer loans. We discuss issues in loan pricing and the expected return from loans. We learn different methods for assessing the credit quality of loans and discuss reasons for default. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-3 Credit Quality Problems Examples of credit quality problems in Australia: –Tricontinental Ltd, –Beneficial Finance Corp. –Rothwells Limited, –Farrow group. In the worst case, credit quality problems can cause FI insolvency or significantly impair the FI’s competitiveness. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

Types of Loan Commercial and industrial loans, Real estate loans, Individual (consumer) loans, Other loans (e.g. government loans, margin loans, farm loans) Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-5 Commercial and Industrial Loans Short-term loans: to finance working capital needs and other short-term funding needs, Long-term loans: to finance purchase of real assets, new venture start-up costs and permanent increases in working capital. Syndicated loans: –Finance provided by a group of lenders, –Usually to finance large C&I loans. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-6 Commercial and Industrial Loans Loan rating: external credit rating. Secured (asset-backed) loan: loan backed by first claim on certain assets. Unsecured loan (junior debt): loan with a general claim only. Spot loan: immediate withdrawal of loan amount. Loan commitment (line of credit): revolving credit facility with a maximum size and a maximum period of time. Commercial paper: unsecured short-term debt instrument. Disintermediation: direct access to funds in capital markets. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-7 Real Estate Loans Loans to individuals and families to finance property purchase. Long maturities, often 25 years. Periodic payments typically cover both interest payments and partial repayment of principal. Changing trend: in the 1990s most real estate loans were granted for owner-occupation; today a larger proportion of loans are for investment purposes. Competitive market through new entrants: RAMS, Aussie Home Loans, Wizard… Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-8 Individual (Consumer) Loans Personal or auto loans. Credit cards are a revolving loan facility. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-9 Calculating the Return on a Loan The Contractually Promised Return on a Loan: Factors affecting the promised loan return: –Loan interest rate, –Fees, –Credit risk premium, –Collateral, –Non-price terms such as compensating balances. Loan rate = base lending rate (BR) + credit risk premium or margin (m). Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-10 Calculating the Return on a Loan The Contractually Promised Return on a Loan: Direct and indirect fees and charges: –Loan origination fee (f), –Compensating balance requirements (b), –Reserve requirement (R). Gross return on loan (k) per dollar lent: 1+k = 1 + [f + (BR+m)] / [1- b(1-R)]. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-11 Calculating the Return on a Loan The Expected Return on a Loan: Expected return might differ from promised return because of default risk. Expected return: E(r) = p(1+k). Where: p = probability of loan repayment. If p < 1: –Default risk exists, –FI needs to set risk premium, –FI needs to recognise that higher fees and charges might decrease p. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-12 Retail Versus Wholesale Credit Decisions Retail: Small dollar size loans, Higher cost associated with collection of information, Usually: accept or reject decision, Standard loan rate is usually charged, Credit risk controlled through credit rationing. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-13 Retail versus Wholesale Credit Decisions Wholesale: Credit risk controls: –Use of interest rates, –Credit quantities. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-14 Default Risk Models Qualitative models: Borrower-specific factors are considered as well as market or systematic factors. Specific factors include: –Reputation: implicit contract, –Leverage (capital structure), –Volatility of earnings, –Collateral. Use of covenants to encourage certain borrower behaviour. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-15 Default Risk Models Qualitative models: Market-specific factors include: –Business cycle, –Level of interest rates. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-16 Default Risk Models Credit Scoring Models: Quantitative models that use observed borrower characteristics to: –Calculate score as a proxy of borrower’s default probability, or –Sort borrowers into different default classes. Scoring models might help to: –Establish factors that help to explain default risk, –Evaluate the relative importance of these factors, –Improve the pricing of default risk, –Sort out bad loan applicants, –More easily calculate reserve needs. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-17 Default Risk Models Credit Scoring Models: Major types of credit scoring models: –Linear probability model, –Logit model, –Linear discriminant models. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-18 Default Risk Models Credit Scoring Models: Linear probability model: –Statistically unsound, since the Zs obtained are not probabilities at all. –Since superior statistical techniques are readily available, little justification for employing linear probability model. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-19 Default Risk Models Credit Scoring Models: Logit model: overcomes weakness of the linear probability model 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. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-20 Default Risk Models Linear Discriminant Models: Altman’s Z score model for manufacturing firms Z=1.2X X X X X 5 Critical values of Z = 1.81 and Z = 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. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-21 Default Risk Models Linear Discriminant Models: Weaknesses: –Broad distinction between borrower categories, i.e. good and bad borrowers, –Weights in any credit scoring model unlikely to be constant over longer periods of time, –Variables in any credit scoring model unlikely to be constant over longer periods of time, –Models ignore hard-to-quantify factors such as borrower reputation, –There is no centralised database on defaulted business loans for proprietary or other reasons. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-22 Newer Models of Credit Risk Measurement and Pricing Term Structure Derivation of Credit Risk: 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 generalised 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. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-23 Newer Models of Credit Risk Measurement and Pricing Term Structure Derivation of Credit Risk: Assume that γ is the proportion of the loan’s principal and interest that is collectible on default: [(1 - p) γ(1 + k)] + [p(1 + k)] = 1 + i Where: (1-p) γ(1+ k) = the payoff the FI expects to get in case of default. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-24 Newer Models of Credit Risk Measurement and Pricing Term Structure Derivation of Credit Risk: If γ > 0, the required risk premium will be less for any (1 - p). K – I = Φ = (1 + i) / (γ + p – pγ) – (1 + i) Where: γ and p are perfect substitutes for each other. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-25 Newer Models of Credit Risk Measurement and Pricing Mortality-Rate Derivation of Credit Risk: 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 = (Total value grade B bond defaults in yr 1 of issue) / (total value grade B bonds outstanding yr 1) MMR 2 = (Total value grade B bond defaults yr 2) / (total value grade B bonds outstanding yr 2) Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-26 Newer Models of Credit Risk Measurement and Pricing RAROC Models: RAROC = risk-adjusted return on capital. Pioneered by Bankers Trust. Essential idea: balancing of expected interest and fee income against expected loan risk. RAROC = one year income on a loan / loan (asset) risk or capital at risk Loan approval if RAROC > benchmark return on capital. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-27 Newer Models of Credit Risk Measurement and Pricing RAROC Models: Problem: measurement of loan risk. Maximum change in credit risk premium can be estimated using publicly available data. ∆R = Max[∆(R i – R G ) > 0]. Where: ∆(R i – R g ) = change in the yield spread between corporate bonds of credit rating class i(R i ) and matched duration Treasury bonds (R G ) over the last year. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-28 Newer Models of Credit Risk Measurement and Pricing RAROC Models: RAROC can look forward as well as backward. Different ways of calculating the dollar capital risk exposure (∆L): RAROC = one year income per dollar loaned / (unexpected default rate × proportion of loan lost on default) Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-29 Probability of Default on a Multi-Period Debt Instrument Important Terms and Concepts: Marginal default probability: the likelihood that a borrower will default in any given year. Cumulative default probability: the likelihood that a borrower will default over a specified multi-year time horizon. No arbitrage means that it is not possible to make a profit without taking any risk. Forward rate: a one-period rate of interest expected on an issued bond at some date in the future. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-30 Probability of Default on a Multi-Period Debt Instrument Assume: 1 - p 1 = 0.04 = marginal default probability in yr p 2 = 0.06 = marginal default probability in yr 2. Survival probability = 0.96 × 0.94 = = 90.24%. Cumulative default probability: Cp = 1 – [(p 1 )(p 2 )] and thus: 1 – (0.96 × 0.94) = 9.76%. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-31 Option Models of Default Risk Use 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 widely. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-32 Option Models of Default Risk Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-33 Option Models of Default Risk Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-34 Option Models of Default Risk 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. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-35 Option Models of Default Risk Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-36 CreditMetrics Basic idea of CreditMetrics: “If next year is a bad year, how much will we lose on our loans and loan portfolio?” VAR one day = P × 1.65 ×  Neither P nor  are observable, as loans are not actively traded. Calculated using: –Data on borrower’s credit rating, –Rating transition matrix, –Recovery rates on defaulted loans, –Yield spreads. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-37 CreditMetrics Example of a transition matrix: Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-38 Credit Risk+ Developed by Credit Suisse Financial Products. Model is 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 frequency distribution: Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher

8-39 Credit Risk+ Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Financial Institutions Management 2e, by Lange, Saunders, Anderson, Thomson and Cornett Slides prepared by Maike Sundmacher