Chapter 11 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:

1. A standardized method, which most community banks are expected to select, and

2. 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:

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)

Chapter 11 13

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)

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 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

Chapter 11 18

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

Chapter 11 19

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

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:

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

Chapter 11 24

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.

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:

1. Return on assets => EBIT/total assets2. Stability of earnings => Inverse of the standard error

of estimate around a 10-year trend in ROA3. Debt service => EBIT/total interest payments4. Cumulative profitability => retained earnings/total

assets5. Liquidity => current assets/current liabilities6. Capitalization => five-year average market value of

firm’s common stock/total long-term capital7. 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:

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

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:

1. The average or expected loss associated with the portfolio and

2. 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