Bank capital ; Bank loans ; Risk ; Bank management

William Perraudin

English

Research Papers in Economics

FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1998 95
Commentary
William Perraudin
I shall divide my comments into three parts: (i) general
thoughts about credit risk modeling and the technical
difficulties involved, (ii) remarks on the implementation of
such models, with particular reference to the papers in this
session by Wilson and by Nishiguchi et al., and (iii) a dis-
cussion of the policy implications of credit risk modeling
and the light shed on this issue by the papers by Jones and
Mingo and by Gray.
BACKGROUND
It is important to understand the background to the cur-
rent interest in credit risk modeling. Recent developments
should be seen as the consequence of three factors. First,
banks are becoming increasingly quantitative in their
treatment of credit risk. Second, new markets are emerging
in credit derivatives, and the marketability of existing
loans is increasing through growth in securitizations and
the loan sales market. Third, regulators are concerned
about improving the current system of bank capital
requirements, especially as it relates to credit risk.
These three factors are strongly self-reinforcing.
The more quantitative approach taken by banks could be
seen as the application of risk management and financial
engineering techniques initially developed in the fixed
income trading area of banks’ operations. However, they
raise the possibility of pricing and hedging credit risk
more generally and encourage the emergence of new
instruments such as credit derivatives. Furthermore, if
banks are adopting a more quantitative approach, regula-
tors may be able to develop more sophisticated and
potentially less distortionary capital requirements for
banking book exposures. However, if regulators do per-
mit the use of models in capital requirement calculations,
banks will have a substantial incentive to invest further
in the development of credit risk models.
The basic problems in developing models of credit
risk are (i) obtaining adequate data and (ii) devising a satis-
factory way of handling the covariability of credit expo-
sures. On data, banks face the difficulty that they have
only recently begun to collect relevant information in a
systematic manner. Many do not even know simple facts
about defaults in their loan books going back in time.
Although serious, this difficulty is transitional and will
be mitigated as time goes by and perhaps as banks make
arrangements to share what data exist. 
The more serious data problem is that bank loans
and even many corporate bonds are either partly or totally
illiquid and mark-to-market values are therefore not
William Perraudin is a professor of finance at Birkbeck College, University of
London, and special advisor to the Regulatory Policy Division of the Bank of
England.96 FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1998
available. This means that one must rely on some other
measure of value in order to establish and track the riski-
ness of credit-sensitive exposures. Two approaches have
been followed by credit risk modelers. J.P. Morgan and
Credit Suisse Financial Products in their respective
modeling methodologies, CreditMetrics and CreditRisk+,
employ ratings and probabilities of ratings transitions as
bases for measuring value and risk. The consulting firm
KMV uses equity price information to infer a borrower’s
underlying asset value and the probability that it will fall
below some default trigger level.
The second major problem faced by credit risk
analysts is that of modeling the covariation in credit risks
across different exposures. It is particularly difficult to do
this in a tractable way while respecting the basic nature
of credit risk, that is, return distributions that are fat-
tailed and highly skewed to the left. Two approaches have
been taken. On the one hand, the CreditMetrics approach
to covariation consists of supposing that ratings transi-
tions are driven by changes in underlying, continuous
stochastic processes. Correlations between these processes
(and hence in ratings transitions) are inferred from corre-
lations in equity returns (to some degree therefore relying
on the KMV methodology). CreditRisk+, on the other
hand, allows parameters of the univariate distributions of
individual exposures to depend on common conditioning
variables (for example, the stage of the economic cycle).
Conditionally, exposures are supposed to be independent,
but unconditionally they are correlated.
IMPLEMENTATIONS OF CREDIT 
RISK MODELING
Two papers in this session represent implementations of
credit risk methods, namely, those by Wilson and by
Nishiguchi et al. The Wilson study describes an approach
to credit risk modeling that resembles CreditRisk+. More
specifically, this approach employs binomial and multino-
mial models of default/no-default events and of movements
between ratings. Correlations between the risks on differ-
ent exposures are incorporated by allowing the probabili-
ties to vary according to whether the macroeconomy is in
one of two states. It is slightly difficult to see how such
a framework would perform in actual applications. For
example, it might be thought of as a problem that the
economy can only be in a boom or a bust. Integrating over
a larger number of states or over some continuous set of
different states might be more natural. 
Although the Wilson paper does discuss ratings
changes, the primary focus (as in CreditRisk+) is on prob-
abilities of default. Credit losses are deemed to occur only
if a borrower defaults and not if, for example, its rating
declines sharply without default taking place. This
approach resembles traditional practices in insurance and
banking markets. By contrast, CreditMetrics takes a more
portfolio-theoretic approach in which losses are registered
as the credit rating of a borrower declines. From an eco-
nomic viewpoint, the portfolio-theoretic approach appears
preferable. For example, it more straightforwardly yields
prescriptions about how a given credit risk may be hedged.
The Nishiguchi et al. paper resembles Credit-
Metrics in that it takes a more portfolio-theoretic
approach. However, in its treatment of correlations, its
approach, like that of Wilson and CreditRisk+, is to allow
exogenous conditioning variables to serve as the source of
covariation in credit risk. Like the Wilson paper, the
Nishiguchi et al. paper does not explore the effectiveness
of the authors’ very complicated approach to modeling
correlation. Since correlations are crucial inputs to the
credit risk measures that come out of such models, a criti-
cal evaluation of the sensitivity of the results to different
approaches would be desirable.
POLICY RELEVANCE
The other two papers in this session, those by Jones and
Mingo and by Gray, provide extremely useful snapshots
of what U.S. and Australian banks, respectively, have
achieved in their implementation of quantitative credit
risk modeling. In both cases, it is notable quite how far
the banks have gotten, although significant obstacles
remain. Substantial efforts have been directed at collecting
data and implementing credit risk measurement sys-
tems. Almost no banks follow a fully portfolio-theoreticFRBNY ECONOMIC POLICY REVIEW / OCTOBER 1998 97
approach. Most employ ratings-based approaches like
CreditMetrics or CreditRisk+ rather than KMV techniques.
Supervisors in both the United States and Australia
have had extensive contact with banks, monitoring
progress and, in the Australian case, coordinating the
exchange of data.
For regulators, a crucial question that Jones and
Mingo, and to some extent Gray, address is whether bank
models are sufficiently developed and comprehensive to
be employed in the calculation of risk-sensitive capital
requirements on banking book exposures. Both studies
are quick to conclude that global use of credit risk models
for the entire banking book is quite infeasible at the current
stage of development of credit risk modeling. Neverthe-
less, both studies view the adoption of such models in
some form as inevitable. The primary argument advanced
by Jones and Mingo is that large U.S. banks currently
engage in substantial “capital arbitrage,” using securiti-
zations and other transactions to cut their capital levels
while retaining the underlying credit risk. A more posi-
tive argument, perhaps, is that by allowing the use of
models, supervisors may reduce distortions in banks’
portfolio choices attributable to the current capital
requirement system, with its unsophisticated approach to
risk weighting.
There are two ways in which credit risk models
could be employed in a limited sense for capital require-
ment calculations. The first would involve their use as a
guide in banking supervision. In their contact with
banks, U.S. supervisors suggest capital add-ons for bank-
ing book assets over and above the Basle 8 percent capital
charge. In the United Kingdom, such add-ons have a
more formal status in that regulators actually require
banks to hold amounts of capital over and above the Basle
8 percent charge. Thus, U.K. banks are required to main-
tain risk-asset ratios for each U.K. bank (that is, the ratio
of broad capital to risk-weighted assets) that exceed bank-
specific trigger ratios. In principle at least, output from
credit risk models could be used as an input to decisions
about such formal or informal capital add-ons.
Second, credit risk models could be employed for
part but not all of the banking book. Jones and Mingo
have a limited discussion of this point. The section of the
banking book to which models might be applied could be
selected either because it is the source of substantial capi-
tal arbitrage or possibly because the assets involved have
stable credit risk on which considerable information is
available. Jones and Mingo presumably have the first of
these two criteria in mind when they argue that certain
transactions involving securitization should be subjected
to modeling. More generally, loans issued by borrowers
that already possess ratings on traded debt or that have
quoted equity might be obvious candidates for credit risk
modeling. Alternatively, some particularly homogeneous
asset categories such as mortgages, personal loans, or
credit card debt may be judged to have stable default
behavior susceptible to credit risk modeling.
CONCLUSION
The papers in the session serve to underline the fact that
credit risk modeling will be a crucial area for regulators
and industry practitioners in coming years. It is hard to
resist the conclusion that models in some shape or form
will be used before too long in bank capital calculations.
As Jones and Mingo argue, the current division of bank
assets between the trading and banking books in and of
itself obliges regulators to consider changes since it pro-
vides banks with strong incentives to reduce capital
requirements through arbitrage. On a more positive note,
making bank capital requirements more sensitive to the
credit risks a bank faces will reduce distortions inherent
in a nonrisk-adjusted system without impairing the main
function of capital requirements, that of bolstering the
stability of the financial system.
The views expressed in this article are those of the author and do not necessarily reflect the position of the Federal Reserve
Bank of New York or the Federal Reserve System. The Federal Reserve Bank of New York provides no warranty, express or
implied, as to the accuracy, timeliness, completeness, merchantability, or fitness for any particular purpose of any information
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Commentary on four papers on credit risk modeling

http://www.newyorkfed.org/research/epr/98v04n3/9810perr.pdf

Commentary on four papers on credit risk modeling

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