1. CHAPTER 12 Credit Risk: Loan Portfolio and Concentration Risk Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.McGraw-Hill/Irwin

2. 12-2 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.

3. 12-3 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

4. 12-4 Web Resources  For information on migration analysis, visit: Standard & Poors www.standardandpoors.com www.standardandpoors.com Moody’s www.moodys.comwww.moodys.com

5. 12-5 Rating Transition Matrix Risk grade: end of year 123Default Risk grade: 1|.85.10.04.01 beginning2|.12.83.03.02 of year3|.03.13.80.04

6. 12-6  Concentration limits –On loans to individual borrower –Concentration limit = maximum loss  loss rate  Maximum 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

7. 12-7 Diversification & Modern Portfolio Theory  Applying portfolio theory to loans –Using loans to construct the efficient frontier –Minimum risk portfolio  Low risk  Low return

8. 12-8 FI Portfolio Diversification

9. 12-9 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

10. 12-10 Modern Portfolio Theory Expected Return: Variance:

11. 12-11 Moody’s KMV Portfolio Manager Model KMV measures these as follows: R i = AIS i - E(L i ) = AIS i - [EDF i × LGD i ]  i = UL i =  Di × LGD i = [EDF i (1-EDF i )] ½ × LGD i  ij = correlation between systematic return components of equity returns of borrower i and borrower j

12. 12-12 KMV Asset Level Correlation

13. 12-13 Partial Applications of Portfolio Theory  Loan volume-based models –Commercial bank call reports  Can be aggregated to estimate national allocations –Shared national credit  National database that breaks commercial and industrial loan volume into 2-digit SIC codes

14. 12-14 Partial Applications  Loan volume-based models –Provide market benchmarks  Standard deviation measure of individual FI’s loan allocations deviation from the benchmark allocations

15. 12-15 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 ]

16. 12-16 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

17. 12-17 Pertinent Websites Bank for International Settlements Federal Reserve Bank Moody’s Moody’s KMV National Association of Insurance Commissioners Standard & Poors www.bis.org www.federalreserve.gov www.moodys.com www.moodyskmv.com www.naic.org www.standardandpoors.com

18. 12-18 *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.

19. 12-19 * 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

20. 12-20 *Credit Risk+ Model: Determinants of Loan Losses