Credit risk evaluation [Elektronische Ressource] : modeling - analysis - management / vorgelegt von Uwe Wehrspohn

Informations

• Publié par: ruprecht-karls-universitat_heidelberg
• Publié le: 01 janvier 2003
• Nombre de lectures: 42
• Langue: English
• Poids de l'ouvrage: 2 Mo

Extrait

Credit Risk Evaluation
Modeling – Analysis – Management





INAUGURAL-DISSERTATION
ZUR ERLANGUNG DER WÜRDE EINES DOKTORS
DER WIRTSCHAFTSWISSENSCHAFTEN
DER WIRTSCHAFTSWISSENSCHAFTLICHEN FAKULTÄT
DER RUPRECHT-KARLS-UNIVERSITÄT HEIDELBERG







VORGELEGT VON
UWE WEHRSPOHN
AUS EPPINGEN

HEIDELBERG, JULI 2002








This monography is available in e-book-format at http://www.risk-and-evaluation.com.

































This monography was accepted as a doctoral thesis at the faculty of economics at Heidelberg University, Ger-
many.
© 2002 Center for Risk & Evaluation GmbH & Co. KG
Berwanger Straße 4
D-75031 Eppingen
www.risk-and-evaluation.com
2
Acknowledgements
My thanks are due to Prof. Dr. Hans Gersbach for lively discussions and many ideas that con-
tributed essentially to the success of this thesis.
Among the many people who provided valuable feedback I would particularly like to thank
Prof. Dr. Eva Terberger, Dr. Jürgen Prahl, Philipp Schenk, Stefan Lange, Bernard de Wit,
Jean-Michel Bouhours, Frank Romeike, Jörg Düsterhaus and many colleagues at banks and
consulting companies for countless suggestions and remarks. They assisted me in creating the
awareness of technical, mathematical and economical problems which helped me to formulate
and realize the standards that render credit risk models valuable and efficient in banks and
financial institutions.
Further, I gratefully acknowledge the profound support from Gertrud Lieblein and Computer
Sciences Corporation – CSC Ploenzke AG that made this research project possible.
My heartfelt thank also goes to my wife Petra for her steady encouragement to pursue this
extensive scientific work.

Uwe Wehrspohn
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Introduction
In the 1990ies, credit risk has become the major concern of risk managers in financial institu-
tions and of regulators. This has various reasons:
• Although market risk is much better researched, the larger part of banks’ economic
capital is generally used for credit risk. The sophistication of traditional standard
methods of measurement, analysis, and management of credit risk might, there-
fore, not be in line with its significance.
• Triggered by the liberalization and integration of the European market, new chan-
nels of distribution through e-banking, financial disintermediation, and the en-
trance of insurance companies and investment funds in the market, the competitive
pressure upon financial institutions has increased and led to decreasing credit mar-
1gins . At the same time, the number of bankruptcies of companies stagnated or in-
2creased in most European countries, leading to a post-war record of insolvencies
3in 2001 in Germany .
• A great number of insolvencies and restructuring activities of banks were influ-
enced by prior bankruptcies of creditors. In the German market, prominent exam-
ples are the Bankgesellschaft Berlin (2001), the Gontard-MetallBank (2002), the
4Schmidtbank (2001), and many mergers among regional banks to avoid insol-
vency or a shut down by regulatory authorities.
The thesis contributes to the evaluation and development of credit risk management methods.
First, it offers an in-depth analysis of the well-known credit risk models Credit Metrics (JP
Morgan), Credit Risk+ (Credit Suisse First Boston), Credit Portfolio View (McKinsey &
5Company) and the Vasicek-Kealhofer-model (KMV Corporation). Second, we develop the
6Credit Risk Evaluation model as an alternative risk model that overcomes a variety of defi-
ciencies of the existing approaches. Third, we provide a series of new results about homoge-
nous portfolios in Credit Metrics, the KMV model and the CRE model that allow to better

1 Bundesbank (2001).
2 Creditreform (2002), p. 4.
3 Creditreform p. 16.
4 Between 1993 and 2000 1,000 out of 2,800 Volks- und Raiffeisenbanken and 142 out of 717 savings banks ceased to
exist in Germany (Bundesbank 2001, p. 59). All of them merged with other banks so that factual insolvency could be
avoided in all cases. Note that shortage of regulatory capital in consequence of credit losses was not the reason for all
of these mergers. Many of them were motivated to achieve cost reduction and were carried out for other reasons.
5 We refer to the Vasicek-Kealhofer-model also as the KMV model.
6 Credit Risk Evaluation model is a trademark of the Center for Risk & Evaluation GmbH & Co. KG, Heidelberg. We re-
fer to the Credit Risk Evaluation model also as the CRE model.
4
understand and compare the models and to see the impact of modeling assumptions on the
reported portfolio risk. Fourth, the thesis covers all methodological steps that are necessary to
quantify, to analyze and to improve the credit risk and the risk adjusted return of a bank port-
folio.
Conceptually, the work follows the risk management process that comprises three major as-
pects: the modeling process of the credit risk from the individual client to the portfolio (the
qualitative aspect), the quantification of portfolio risk and risk contributions to portfolio risk
as well as the analysis of portfolio risk structures (the quantitative aspect), and, finally, meth-
ods to improve portfolio risk and its risk adjusted profitability (the management aspect).
The modeling process
The modeling process includes the identification, mathematical description and estimation of
influence factors on credit risk. On the level of the single client these are the definitions of
7 8 9default and other credit events , the estimation of default probabilities , the calculation of
10 11credit exposures and the estimation of losses given default . On the portfolio level, depend-
12encies and interactions of clients need to be modeled .
The assessment of the risk models is predominantly an analysis of the modeling decisions
taken and of the estimation techniques applied. We show that all of the four models have con-
siderable conceptual problems that may lead to an invalid estimation, analysis and pricing of
portfolio risk.
In particular, we identify that the techniques applied for the estimation of default probabilities
13 14and related inputs cause systematic errors in Credit Risk+ and Credit Portfolio View if
certain very strict requirements on the amount of available data are not met even if model
assumptions are assumed to hold. If data is sparse, both models are prone to underestimate
default probabilities and in turn portfolio risk.
For Credit Metrics and the KMV model, it is shown that both models lead to correct results if
they are correctly specified. The concept of dependence that is common to both models –
called the normal correlation model – can easily be generalized by choosing a non-normal

7 See section I.A.
8 I.e. of rating transitions, see sections I.B.4, I.B.6.c)(4), I.B.7.
9 See section I.B.
10 Section I.C.
11 Section I.D.
12 See Section II.A.
13 Section I.B.5
14 Section I.B.6
5
distribution for joint asset returns. As one of the main results, we prove for homogenous port-
folios that the normal correlation model is precisely the risk minimal among of all possible
generalizations of this concept of dependence. This implies that even if the basic concept of
dependence is correctly specified, Credit Metrics and the Vasicek-Kealhofer model systemati-
cally underestimate portfolio risk if there is any deviation from the normal distribution of as-
set returns.
15Credit Risk+ has one special problem regarding the aggregation of portfolio risk . It is the
only model whose authors intend to avoid computer simulations to calculate portfolio risk and
attain an analytical solution for the portfolio loss distribution. For this reason, the authors
choose a Poisson approximation of the distribution of the number of defaulting credits in a
portfolio segment. As a consequence each segment contains an infinite number of credits.
This hidden assumption may lead to a significant overestimation of risk in small segments,
e.g. when the segment of very large exposures in a bank portfolio is considered that is usually
quite small. Thus, Credit Risk+ is particularly suited for very large and homogenous portfo-
lios. However, at high percentiles, the reported portfolio losses even always exceed the total
portfolio exposure.
With the Credit Risk Evaluation model, we present a risk model that avoids these pitfalls and
integrates a comprehensive set of influence factors on an individual client’s risk and on the
portfolio risk. In particular, the CRE model captures influences on default probabilities and
dependencies such as the level of country risk, business cycle effects, sector correlations and
individual dependencies between clients. This leads to an unbiased and more realistic estim