1 / 45

CHAPTER 11

CHAPTER 11. Modern Methods For Analyzing and Managing Credit. 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

conan-brewer
Download Presentation

CHAPTER 11

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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 Chapter 11

  3. 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 Chapter 11

  4. 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). Chapter 11

  5. Why a Renewed Interest? • TRICK • -Ization factors Chapter 11

  6. Components of TRICK Recall: • Transparency • Risk Exposure • Information Technology • Customers • Kapital Adequacy Chapter 11

  7. 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. Chapter 11

  8. 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. Chapter 11

  9. 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. Chapter 11

  10. 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. Chapter 11

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

  12. Reorganization of the Bank Lending Function • The treatment of the loan product is moving toward that of bonds, which means emphasis on: • Present value or price as discounted future cash flows • Probability of default (d) and default risk • Recovery rates () • Prepayment risk • External ratings (Moody’s and S&P) Chapter 11

  13. Modern Portfolio Theory • Two important and recent developments in bank loan portfolios focus on loan-portfolio models designed to: • Identify the efficient loan portfolio and determine how to move toward it, and • To estimate the amount of economic capital needed to support the loan portfolio, e.g., RAROC (Ch. 10) Chapter 11

  14. 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) Chapter 11

  15. 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 Chapter 11

  16. 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 Chapter 11

  17. 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 Bank Loans Versus Bonds Chapter 11

  18. Hybrid Products • Modified collateral • Longer maturity • Covenant light • Relationship banks amend • Light call protection • Floating rate Chapter 11

  19. 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 Bond Markets Versus Bank Loan Markets Chapter 11

  20. 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? Chapter 11

  21. Quantitative and Technical Approaches • Classification models • Value-at-risk (VAR) • Credit derivatives Chapter 11

  22. 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 Chapter 11

  23. 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: • Use under very general conditions • Ease of understanding, and in some cases • Ease of computation • Figure 11-2 (p. 362), cash flow and leverage Chapter 11

  24. Loan-Classification Models:Risk Categories • Current – Loan is being paid back on schedule and perceived to be an acceptable banking risk. • Especially Mentioned – Loan has some minor problem (e.g., incomplete documentation) requiring it to be “criticized”. • Substandard – Loan has weaknesses presenting some chance of default. • Doubtful – Loan has considerable weakness and the bank is likely, say 50% chance, of sustaining a loss. • Loss – Loan is deemed to be uncollectible. Such loans are usually written or charged off. Chapter 11

  25. Zeta Analysis • Model for identifying the bankruptcy risk of corporations. The following seven variables were good discriminators between failed and nonfailed business firms: • Return on assets => EBIT/total assets • Stability of earnings => Inverse of the standard error of estimate around a 10-year trend in ROA • Debt service => EBIT/total interest payments • Cumulative profitability => retained earnings/total assets • Liquidity => current assets/current liabilities • Capitalization => five-year average market value of firm’s common stock/total long-term capital • Size => firm’s total assets Chapter 11

  26. 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 Chapter 11

  27. A Loan-Surveillance Model • A logit model: P = 1/(1 + e-y), P = the probability of noncompliance • Y = ΣbiXi • 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 Chapter 11

  28. 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 < 0.5 => Compliance group Chapter 11

  29. 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: • The inability to quantify the customer-relationship aspect of the lending process • The reluctance of lenders to share information with researchers (under the guise of protecting customer confidentiality) • Even if such information sharing did occur, there is a lack of data on rejected borrowers • The backward-looking nature of classification studies Chapter 11

  30. 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 Chapter 11

  31. 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 Chapter 11

  32. 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 Chapter 11

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

  34. 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 Chapter 11

  35. 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 Chapter 11

  36. KMV’s Expected Default Frequency (EDFTM) • 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 Chapter 11

  37. 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 Chapter 11

  38. Practical Implications for Loan Portfolio Management Managers of portfolios subject to default risk have two major concerns: • The average or expected loss associated with the portfolio and • The range or distribution of possible losses about that expectation. Chapter 11

  39. 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 Chapter 11

  40. 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) Chapter 11

  41. 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) Chapter 11

  42. 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 Chapter 11

  43. 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 Chapter 11

  44. 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 Chapter 11

  45. 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 Chapter 11

More Related