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Chapter 111 CHAPTER 11 Modern Methods For Analyzing and Managing Credit.

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Presentation on theme: "Chapter 111 CHAPTER 11 Modern Methods For Analyzing and Managing Credit."— Presentation transcript:

1 Chapter 111 CHAPTER 11 Modern Methods For Analyzing and Managing Credit

2 Chapter 112 LEARNING OBJECTIVES To understand … 1. Why a renewed interest in credit risk exists 2. The importance of securitization and the reorganization of the bank lending function 3. Modern portfolio theory applied to bank loan portfolios 4. The more quantitative and technical approaches to management of loan portfolios and credit risk (e.g., VAR) 5. Credit derivatives

3 Chapter 113 CHAPTER THEME This chapter focuses on modern methods for analyzing credit risk Portfolio theory and other sophisticated quantitative techniques provide the foundation for this approach Securitization and credit derivatives represent examples of such techniques

4 Chapter 114 A RENEWED INTEREST IN CREDIT RISK Saunders [1999] captures the thrust of this new movement: “In recent years, a revolution has been brewing in the way credit risk is both measured and managed. Contradicting the relatively dull and routine history of credit risk, new technologies and ideas have emerged among a new generation of financial-engineering specialists, who are applying their model-building skills and analysis to this area” (p. 1).

5 Chapter 115 Why a Renewed Interest? TRICK -Ization factors

6 Chapter 116 Components of TRICK Recall: Transparency Risk Exposure Information Technology Customers Kapital Adequacy

7 Chapter 117 Transparency Traditionally, bank business loans have been opaque. The process of securitization has contributed to the renewed interest in credit. Innovative developments of value at risk (VAR) and credit derivatives has lead to greater transparency and more rational pricing of credit.

8 Chapter 118 Risk Exposure Increased bankruptcies, both corporate and personal, are reasons for a renewed interest in credit risk. When collateral values deteriorate and become more volatile, these changes get lenders’ attention.

9 Chapter 119 Information Technology The potential for “riskmetrics” techniques to be applied as “creditmetrics” procedures has sparked a renewed interest in credit risk. This increased quantitative and technical approach to credit management and analysis has attracted financial engineers to the field. Also, credit derivatives has renewed interest in credit risk.

10 Chapter 1110 Customers As debt instruments such as corporate bonds and commercial paper has expanded, banks have been pressured to find new customers. This greater exposure to default risk has been another driver in the renewed interest in credit risk.

11 Chapter 1111 Kapital Adequacy The revised Basle Accord further heightens the interest in credit risk by offering banks three ways of calculating minimum capital requirements: 1. A standardized method, which most community banks are expected to select, and 2. Two internal ratings-based methods

12 Chapter 1112 Reorganization of the Bank Lending Function The treatment of the loan product is moving toward that of bonds, which means emphasis on: 1. Present value or price as discounted future cash flows 2. Probability of default (d) and default risk 3. Recovery rates ( ) 4. Prepayment risk 5. External ratings (Moody’s and S&P)

13 Chapter 1113 Modern Portfolio Theory Two important and recent developments in bank loan portfolios focus on loan-portfolio models designed to: 1. Identify the efficient loan portfolio and determine how to move toward it, and 2. To estimate the amount of economic capital needed to support the loan portfolio, e.g., RAROC (Ch. 10)

14 Chapter 1114 The Current State of Credit Risk and Portfolio Management Areas of Loan-Portfolio Management Business Strategy Risk Grading (i.e., rating a loan as in a bond rating) Risk Pricing (e.g., RAROC) Portfolio Grooming (e.g., rebalancing by selling and buying loans) Risk-management organization and governance (e.g., CREDCO)

15 Chapter 1115 Categories of Loan-Portfolio Managers (survey results) Passive traditionalists (19 out of 64 banks): They accept market pricing and hold almost everything they underwrite Active traditionalists (30 out of 64): They use risk grading, risk pricing, and measures of product/customer profitability Semi-advanced practitioners (11 out of 64): They practice a business strategy with more flexible risk limits and develop solutions to poor market pricing Advanced practitioners (4 out of 64): They are on the cutting edge of loan-portfolio management in terms of the five areas on the previous slide

16 Chapter 1116 The Evolutionary Path of Credit Portfolio Management Four risk-altering techniques include: 1. Risk grading and pricing to reduce mispriced underwriting 2. Sell/syndicate loans 3. Buy loans of others 4. Use credit derivatives

17 Chapter 1117 Bank Loans Versus Bonds Bank loans Senior secured Shorter maturity More covenants Often amended Freely callable Floating rate Bonds Unsecured, subordinate Longer maturity Fewer covenants Difficult to amend Call protected Fixed rate

18 Chapter 1118 Hybrid Products Modified collateral Longer maturity Covenant light Relationship banks amend Light call protection Floating rate

19 Chapter 1119 Bond Markets Versus Bank Loan Markets Bond markets Underwriters/issuers Investors Rating agencies These parties are pervasively independent Loan markets Bankers foster functional “independence” among credit groups, customers, and portfolio managers

20 Chapter 1120 Implications for Loan Quality Bank loans should be safer and therefore have lower yields Recovery rates should be higher for intermediated debt than for bonds How will subprime lending affect loan quality?

21 Chapter 1121 Quantitative and Technical Approaches Classification models Value-at-risk (VAR) Credit derivatives

22 Chapter 1122 Classification Models Classification models (also called “artificial intelligence”) are statistical devices that can be used as tools to complement decision-making These models are designed to replicate the decisions of an expert in the field They are best viewed as tools or aids to decision-making

23 Chapter 1123 Decision Trees Decision trees can be used to develop binary classification rules to assign observations to a priori groups (e.g., bankrupt vs. nonbankrupt or good loan vs. bad loan). The main advantages of these tests are: 1. Use under very general conditions 2. Ease of understanding, and in some cases 3. Ease of computation  Figure 11-2 (p. 362), cash flow and leverage

24 Chapter 1124 Loan-Classification Models: Risk Categories 1. Current – Loan is being paid back on schedule and perceived to be an acceptable banking risk. 2. Especially Mentioned – Loan has some minor problem (e.g., incomplete documentation) requiring it to be “criticized”. 3. Substandard – Loan has weaknesses presenting some chance of default. 4. Doubtful – Loan has considerable weakness and the bank is likely, say 50% chance, of sustaining a loss. 5. Loss – Loan is deemed to be uncollectible. Such loans are usually written or charged off.

25 Chapter 1125 Zeta Analysis Model for identifying the bankruptcy risk of corporations. The following seven variables were good discriminators between failed and nonfailed business firms: 1. Return on assets => EBIT/total assets 2. Stability of earnings => Inverse of the standard error of estimate around a 10-year trend in ROA 3. Debt service => EBIT/total interest payments 4. Cumulative profitability => retained earnings/total assets 5. Liquidity => current assets/current liabilities 6. Capitalization => five-year average market value of firm’s common stock/total long-term capital 7. Size => firm’s total assets

26 Chapter 1126 A Z-Score (1968 Model) for Strategic Electronics Corp. (Ch. 10) Z = 1.2(0.5683) + 1.4(0.6307) + 1.4(0.0642 + 3.3(0.686)* + 1.0(1.2817) = 3.4701 > 2.675 => Nonbankrupt-group prediction *Based on ratio of book value of equity to book value of total debt as MVE is not available

27 Chapter 1127 A Loan-Surveillance Model A logit model: P = 1/(1 + e -y ), P = the probability of noncompliance Y = Σb i X i Intercept = -2.04 X1 = (Cash + mkt sec)/TA = -5.24 X2=Net sales/(cash + mkt sec)= 0.005 X3 = EBIT/TA = -6.65 X4 = Total debt/TA = 4.40 X5 = Fixed assets/NW = 0.079 X6 = Working cap/net sales = -0.102

28 Chapter 1128 Loan Surveillance for SEC (Ch. 10) X1 = 0.043 X2 = 30.03 X3 = 0.064 X4 = 0.274 X5 = 1.458 X6 = 0.0828 y = -1.53 and P = 0.18 Compliance group

29 Chapter 1129 Reconciling Research and Practice in Commercial Lending Four major drawbacks to closing the gap between research (defined as theory and empirical models) and practice remain: 1. The inability to quantify the customer-relationship aspect of the lending process 2. The reluctance of lenders to share information with researchers (under the guise of protecting customer confidentiality) 3. Even if such information sharing did occur, there is a lack of data on rejected borrowers 4. The backward-looking nature of classification studies

30 Chapter 1130 Value-at-Risk (VAR) J.P. Morgan’s value-at-risk methodology, also known as “riskmetrics” was introduced in 1994 Development was prompted by the R and K in TRICK J.P. Morgan wanted a daily measure of the risk exposure (R) in the bank’s trading portfolio

31 Chapter 1131 The Intuition of VAR and Extension to Credit Risk Key ingredients in VAR 1. Expected maximum loss or worst-case scenario 2. Target time horizon 3. Confidence level or interval

32 Chapter 1132 Objectives and Complexity of Credit-Risk Models Can CreditMetrics do for credit risk what RiskMetrics did for market risk? Inputs needed to estimate market value for bank loans 1. External credit ratings 2. Probability of a rating change 3. Recovery rates for defaulted loans 4. Loan rates and credit spreads

33 Chapter 1133 Credit Events, VAR Calculations, and Distributions of Loan Values Table 11-3, p. 371 Figure 11-3, p. 372

34 Chapter 1134 Three-Stage Approach to Calculating VAR due to Credit Risk Stage 1 – Focuses on exposures including facilities, commitments, bond positions, receivables, and OBSAs Stage 2 – Focuses on VAR due to credit Stage 3 – Highlights correlations, rating services, and equity series with emphasis on models (e.g., correlations) and joint credit-quality probabilities

35 Chapter 1135 Issues and Problems Validation of the risk measure (Basel) The correlation problem (industry concentration) Creditworthiness and the probability of default, d = f(credit rating, maturity…) Rating migration likelihoods (transition matrices) Credit quality vs. rating changes

36 Chapter 1136 KMV’s Expected Default Frequency (EDF TM ) Figures 11-4 and 11-5, pp. 375-376 EDF is the area in a probability distribution in which the market value of assets falls below the par value of debt, that is, where default occurs KMV sells two products: EDF measures and portfolio-management tools Option-pricing framework

37 Chapter 1137 Critique of Rating Changes Expected (based on historical averages) and actual default rates can differ Default rates overlap within rating categories Rating changes are not timely

38 Chapter 1138 Practical Implications for Loan Portfolio Management Managers of portfolios subject to default risk have two major concerns: 1. The average or expected loss associated with the portfolio and 2. The range or distribution of possible losses about that expectation.

39 Chapter 1139 Credit Derivatives A credit derivative is an over-the-counter, off-balance sheet contract the value of which is derived, directly or indirectly, from the price of a credit instrument Situation that credit derivatives protect against are called “credit events” and include the following: Payment default on a specific “reference asset” of the “reference party” Payment default on designated financial obligations of the reference party Bankruptcy of the reference party

40 Chapter 1140 Credit Swaps: The Most Popular Credit Derivative Two types: 1. A pure-credit or default swap: pay a premium to protect against an adverse credit event Contract components Notional amount Term or maturity Reference party (whose credit is traded) Reference asset Hedge ratio = LIED(loan)/LIED(bond)

41 Chapter 1141 Credit Swaps (continued) 2. Total-return swap: More complicated as it involves an element of market risk associated with interest-rate movements. It is in this sense that a total-return swap is not a “pure credit swap” Example (p. 379)

42 Chapter 1142 Pricing Credit Swaps Credit spread = compensation for bearing risk Credit-swap price = compensation for bearing risk Credit-swap price ~ credit spread – credit charge Credit-swap price ~ credit spread (for a risk-free counterparty

43 Chapter 1143 Pricing Risky Counterparties Three ingredients required: 1. Yield curve for the risky swap counterparty 2. An estimate of the correlation between default by the reference asset and default by the swap party 3. Recovery rate for the risky swap counterparty

44 Chapter 1144 Potential Weaknesses and Pitfalls in the Modern Methods “Hunters”, “skinners”, and traders Will traders care about relationships and credit quality? If you don’t like the loan, sell it! Taleb’s critique of VAR (“charlatanism”) Alternatives to VAR: reduce leverage, better diversification, and less reliance on dynamic hedging

45 Chapter 1145 CHAPTER SUMMARY Structural changes in financial markets and the bank lending function coupled with advances in financial engineering have generated a renewed interest in credit risk TRICK -ization factors Modern portfolio theory, quantitative techniques and models, and credit derivatives

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