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Über dieses Buch

In the last decade the banking industry has experienced a significant development in the understanding of credit risk. Refined methods were proposed concerning the estimation of key risk parameters like default probabilities. Further, a large v- ume of literature on the pricing and measurement of credit risk in a portfolio c- text has evolved. This development was partly reflected by supervisors when they agreed on the new revised capital adequacy framework, Basel II. Under Basel II, the level of regulatory capital depends on the risk characteristics of each credit while a portfolio context is still neglected. The focus of this book is on the estimation and validation of the three key Basel II risk parameters, probability of default (PD), loss given default (LGD), and ex- sure at default (EAD). Since the new regulatory framework will become operative in January 2007 (at least in Europe), many banks are in the final stages of imp- mentation. Many questions have arisen during the implementation phase and are discussed by practitioners, supervisors, and academics. A ‘best practice’ approach has to be formed and will be refined in the future even beyond 2007. With this book we aim to contribute to this process. Although the book is inspired by the new capital framework, we hope that it is valuable in a broader context. The three risk parameters are central inputs to credit portfolio models or credit pricing al- rithms and their correct estimation is therefore essential for internal bank contr- ling and management.

Inhaltsverzeichnis

Frontmatter

I.. Statistical Methods to Develop Rating Models

Without Abstract
Evelyn Hayden, Daniel Porath

II.. Estimation of a Rating Model for Corporate Exposures

6. Conclusions
This chapter focused on the special difficulties that are encountered when developing internal rating models for corporate exposures. Although the whole process with data collection and processing, model building and validation usually takes quite some time and effort, the job is not yet completed with the implementation of the derived rating model. The predictive power of all statistical models depends heavily on the assumption that the historical relationship between the model’s covariates and the default event will remain unchanged in the future. Given the wide range of possible events such as changes in firms’ accounting policies or structural disruptions in certain industries, this assumption is not guaranteed over longer periods of time. Hence, it is necessary to revalidate and eventually recalibrate the model regularly in order to ensure that its predictive power does not diminish.
Evelyn Hayden

III.. Scoring Models for Retail Exposures

Without Abstract
Daniel Porath

IV.. The Shadow Rating Approach — Experience from Banking Practice

Without Abstract
Ulrich Erlenmaier

V.. Estimating Probabilities of Default for Low Default Portfolios

Without Abstract
Katja Pluto, Dirk Tasche

VI.. A Multi-Factor Approach for Systematic Default and Recovery Risk

Without Abstract
Daniel Rösch, Harald Scheule

VII.. Modelling Loss Given Default: A “Point in Time”-Approach

Without Abstract
Alfred Hamerle, Michael Knapp, Nicole Wildenauer

VIII.. Estimating Loss Given Default — Experiences from Banking Practice

Without Abstract
Christian Peter

IX.. Overview of EAD Estimation Concepts

3. Conclusion
This article presented basic concepts for estimating EAD for balance-sheet and off-balance-sheet financial products. We started with the description of the methods, which are delivered by the regulatory framework. If we look at the various shortcomings of the regulatory methods, we motivated how internal methods for EAD-estimation should be designed to avoid these disadvantages and create more elaborate techniques to estimate the EAD in an economic sense. For estimating the EAD for derivative portfolios various Monte-Carlo techniques can be applied.
Walter Gruber, Ronny Parchert

X.. EAD Estimates for Facilities with Explicit Limits

Without Abstract
Gregorio Moral

XI.. Validation of Banks’ Internal Rating Systems - A Supervisory Perspective

Without Abstract
Stefan Blochwitz, Stefan Hohl

XII.. Measures of a Rating’s Discriminative Power — Applications and Limitations

Without Abstract
Bernd Engelmann

XIII.. Statistical Approaches to PD Validation

Without Abstract
Stefan Blochwitz, Marcus R. W. Martin, Carsten S. Wehn

XIV.. PD-Validation — Experience from Banking Practice

8. Conclusion
In this chapter we dealt with validation of rating systems, constructed to forecast a 1-year probability of default. Hereby, we focused on statistical tests and their application for bank internal purposes, especially in the Basel II periphery. We built up a simulation based framework to take account of dependencies in defaults (asset correlation), which additionally has the potential to appraise the type II error, i.e. the non-detection of a bad rating system, for optional scenarios. Hereby, the well known exact and approximated binomial test and the Hosmer-Lemeshow-x 2 test are used, but we also introduced the less popular Spiegelhalter test and an approach called simultaneous binomial test, which allow the testing of a complete rating system and not just each grade separately. As it is important for banks to compare the quality of modules of their rating system, we also refer to the Redelmeier test. As for any applied statistical method, building test samples is an important issue. We designed the concept of “the rolling 12-months-window” to fulfil the Basel II and bank’s internal risk management requirements as well as using the bank’s IT-environment (rating database) effectively and is in harmony with our definition of what a rating should reflect, namely the bank’s most accurate assessment of the 1-year-PD of a borrower. All concepts are demonstrated with a very up-to-date, real-life bank internal rating data set in detail.
We focus mainly on statistical concepts for rating validation (backtesting) but it has to be emphasised that for a comprehensive and adequate validation in the spirit of Basel II, much more is required. To name a few, these include adherence of defined bank internal rating processes, accurate and meaningful use of ratings in the bank’s management systems and correct implementation in the IT-environment.
Robert Rauhmeier

XV.. Development of Stress Tests for Credit Portfolios

Without Abstract
Volker Matthias Gundlach

Backmatter

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