Chapter 12
Credit Risk: Loan
Portfolio and
Concentration Risk
McGraw-Hill/Irwin
© 2008 The McGraw-Hill Companies, Inc., All Rights Reserved.
Overview

12-2
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.
12-3
Simple Models of Loan Concentration

Migration analysis



Track credit rating changes within sector or pool
of loans.
Rating transition matrix.
Widely applied to commercial loans, credit
card portfolios and consumer loans.
Web Resources

12-4
For information on migration analysis, visit:
Standard & Poors
www.standardandpoors.com
Moody’s www.moodys.com
Rating Transition Matrix
Risk grade:
beginning
of year
Risk grade: end of year
1
2
3
Default
1|
.85 .10 .04 .01
2|
.12 .83 .03 .02
3|
.03 .13 .80 .04
12-5
Simple Models of Loan Concentration

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
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.
Applying Portfolio Theory to Loans

12-8
Require



(i) expected return on loan (measured by all-inspread);
(ii) loan risk;
(iii) correlation of loan default risks.
Modern Portfolio Theory
Expected Return:
n
R p   X i Ri
i 1
Variance:
n
n
n
   X    X i X j i , j
2
p
i 1
n
n
2
i
2
i
i 1 j 1
  X i X j i , j i j
i 1 j 1
12-9
FI Portfolio Diversification
12-10
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.
12-11
Partial Applications of Portfolio Theory

12-12
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.
Partial Applications

12-13
Loan volume-based models (continued)

Provide market benchmarks.

Standard deviation measure of individual FI’s loan
allocations deviation from the benchmark allocations.
N
j 
2
(
X

X
)
 i, j
i
i 1
N
Loan Loss Ratio-Based Models

Estimate loan loss risk by SIC sector.

Time-series regression:
[sectoral losses in ith sector]
[ loans to ith sector ]
= a + bi [total loan losses]
[ total loans ]
12-14
Regulatory Models

Credit concentration risk evaluation largely
subjective and based on examiner
discretion.


12-15
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.
Pertinent Websites
For more information visit:
Bank for International Settlements
www.bis.org
Federal Reserve Bank
www.federalreserve.gov
KMV www.kmv.com
Moody’s www.moodys.com
National Association of Insurance
Commissioners www.naic.org
Standard & Poors
www.standardandpoors.com

12-16
*CreditMetrics
12-17
“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.
* 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.

12-18
*Credit Risk+ Model: Determinants of
Loan Losses
12-19