CHAPTER 12

Credit Risk: Loan Portfolio and Concentration Risk

Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.McGraw-Hill/Irwin

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Overview

This chapter discusses the management of credit risk in a loan (asset) portfolio context. It also discusses the setting of credit exposure limits to industrial sectors and regulatory approaches to monitoring credit risk. The National Association of Insurance Commissioners has also developed limits for different types of assets and borrowers in insurers’ portfolios.

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Simple Models of Loan Concentration

Migration analysis– Track credit rating changes within sector

or pool of loans– Rating transition matrix reflects history of

ratings changes Widely applied to commercial loans,

credit card portfolios, and consumer loans

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Web Resources

For information on migration analysis, visit:Standard & Poors www.standardandpoors.comMoody’s www.moodys.com

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Rating Transition Matrix

Risk grade: end of year

1 2 3 DefaultRisk grade: 1| .85 .10 .04 .01beginning 2| .12 .83 .03 .02of year 3| .03 .13 .80 .04

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Concentration limits – On loans to individual borrower– Concentration limit = maximum loss

loss rateMaximum loss expressed as percent of capital

– Some countries, such as Chile, specify limits by sector or industry

– FIs typically set geographic concentration limits

Simple Models of Loan Concentration

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Diversification & Modern Portfolio Theory

Applying portfolio theory to loans– Using loans to construct the efficient

frontier– Minimum risk portfolio

Low riskLow return

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FI Portfolio Diversification

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Applying Portfolio Theory to Loans

Requires: – (i) Expected return on loan (typically

measured by the all-in-spread)– (ii) Loan risk– (iii) Correlation of loan default risks

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Modern Portfolio Theory

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Moody’s KMV Portfolio Manager Model

KMV measures these as follows:Ri = AISi - E(Li) = AISi - [EDFi × LGDi]

i = ULi = Di × LGDi = [EDFi(1-EDFi)]½ ×

LGDi

ij = correlation between systematic return components of

equity returns of borrower i and borrower j

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KMV Asset Level Correlation

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Partial Applications of Portfolio Theory Loan volume-based models

– Commercial bank call reportsCan be aggregated to estimate national

allocations

– Shared national creditNational database that breaks commercial

and industrial loan volume into 2-digit SIC codes

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Partial Applications Loan volume-based models

– Provide market benchmarksStandard deviation measure of individual FI’s

loan allocations deviation from the benchmark allocations

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Loan Loss Ratio-Based Models

Estimate loan loss risk by SIC sector– Time-series regression:

[sectoral losses in ith sector] [ loans to ith sector ]

= + i [total loan losses]

[ total loans ]

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Regulatory Models Credit concentration risk evaluation largely

subjective and based on examiner discretion– Quantitative models were rejected by

regulators because the methods were not sufficiently advanced and available data were not sufficient

Life and PC insurance regulators propose limits on investments in securities or obligations of any single issuer– General diversification limits

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Pertinent WebsitesBank for International

Settlements Federal Reserve

Bank Moody’sMoody’s KMVNational Association

of InsuranceCommissioners

Standard & Poors

www.bis.org

www.federalreserve.gov

www.moodys.comwww.moodyskmv.comwww.naic.org

www.standardandpoors.com

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*CreditMetrics If next year is a bad year, how much will

I lose on my loans and loan portfolio?VAR = P × 1.65 ×

Neither P, nor observed Calculated using:

– (i)Data on borrower’s credit rating; (ii) Rating transition matrix; (iii) Recovery rates on defaulted loans; (iv) Yield spreads.

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* 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

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*Credit Risk+ Model: Determinants of Loan Losses