Credit risk. Measurement, evaluation and management (Q1411807)

This book contains the selected papers presented at the 8th Economic Workshop in Karlsruhe, held on March 2002 at the Faculty of Economics, Karlsruhe, Germany. Organized jointly by the Institute of Statistics and Mathematical Economics and the DZ-Bank AG, Frankfurt am Main, the 200 participants at the Workshop discussed the new developments in measurement, evaluation, and management of the credit risk. The contents of the 15 selected contributions of this really important conference are presented briefly in what follows: (1) A. Benin, S. Trück and S. T. Rachev: (Approaches to Credit Risk in the New Basel Capital Accord) present the main features of the new Basel Capital Accord (Basel II) concerning the regulatory measurement of Credit Risk. The determining aspects of the approaches to credit risk in the new capital accord are surveyed: the standardization approach (STD) as well as the two forms of the internal rating based (IRB) approach (foundation and advanced). Further comments on several features of Basel II and its possible changes in the final version of the accord are illustrated. (2) C. Bluhm and L. Overbeck (Systematic Risk in Homogeneous Credit Portfolios) use the CreditMetrics/KMV one-factor model to discuss the quantification of systematic risk by estimation of asset correlations in homogeneous portfolios. Based on this concept, the Basel-II proposal to fix asset correlation for corporate loans at the 20\% level is compared with empirical data. (3) D. D'Souza, K. Amir-Atefi and B. Racheva-Jotova: (Valuation of a Credit Default Swap: The Stable Non-Gaussian vs. the Gaussian Approach) investigate empirically the effect of different distributional assumptions governing defaultable bond price uncertainty on the price of a credit default swap. The investigation tools used are the two-factor Hull-White-model and the extension of the fractional recovery model of Duffle-Singleton (1991) given by P. Schönbucher (1999). Prices are compared under the assumption of a Gaussian and a non-Gaussian distribution for the underlying risk factor. (4) C. Heidelbach and W. Kürzinger: (Basel-II in the DaimlerChrysler Bank) point out the consequences of the Basel II Accord on a Bank with the special profile as provider of automotive financial services, as it is DaimlerChrysler Bank. (5) A. Karmann and Maltritz: (Sovereign Risk in a Structural Approach. Evaluating Sovereign Ability-to-Pay and Probability of Default) present a quantificatiorn of the probability that a nation will default on repayment obligations in foreign currency. The authors adopt the approach introduced by R. C. Merton (1974), considering as underlying process the nation's ability-to-pay modelled by the sum of future payment surpluses. The method is successfully demonstrated on examples from Latin America and Russia. (6) R. Kiesel, W. Perraudin and A. Taylor: (An Extreme Analysis of VaRs for Emerging Market Benchmark Bonds) examine the practical aspects of using techniques from Extreme Value Theory (EVT) to estimate Value-at Risk (VaR). The performance of EVT estimators is compared with the VaR estimators which are based on quantiles of empirical return distributions. The two types of stimators are shown to yield almost identical results for commonly used confidence levels. (7) D. Kluge and F. Lehrbass: (Default Probabilities in Structured Commodity Finance) consider the investment decisions based on cash flow modelling and point out that the default risk of a project depends actually on the ``combined downside risk'' of spot price and production level movements. A workable approach to the measurement of this risk is presented. (8) F. Landskog, A. McNeil and U. Schmock: (Kendall's Tau for Elliptical Distributions), on the basis of known properties of elliptical distributions, prove that the relationship between Kendall's Tau and the linear correlation coefficient for bivariate normal distributions can be generalized to the class of elliptical distributions. Applications of this result are pointed out in the context of multivariate financial time series models and portfolio credit risk models. (9) M. Müller and W. Härdle: (Exploring Credit Data) introduce semi-parametric extensions of the logit-model, which allow nonlinear relationships. The technique is based on the theory of generalized partial linear models and is used for credit risk scoring and the estimation of default probabilities. (10) B. Racheva-Jotova, S. Stoyanov and S. T. Rachev: (Stable Non-Gaussian Credit Risk Model; The Cognity Approach) illustrate a new approach for integrated market and credit risk management using Cognity software. The Cognity Credit Risk System comprises two models, the Asset Value Approach and the Stochastic Default Rate Model, both based on Stable distributions. (11) T. Rempel-Oberem, R. Klingeler and P. Martin: (An Application of the CreditRisk\(^+\) Model) discuss a novel approach, based on the CreditRisk\(^+\) model, for the determination of default events which was developed for two different credit institutions. (12) Ingo Schäl: (Internal Ratings for Corporate Clients) exposes principles of internal rating systems for corporate clients and presents practical experience for building such systems. The author describes how the quality of rating systems can be measured, and gives basic requirements for backtesting. (13) P. Schlottmann and D. Seese (Finding Constrained Downside Risk-Return Efficient Credit Portfolio Structures Using Hybrid Multiobjective Evolutionary Computation) show that risk analysis requires the computation of a set of Pareto-efficient portfolio structures in a nonlinear, non-convex setting with additional constraints. The authors developed a new fast and flexible framework for solving this problem, advantageous empirical results supporting the proposed procedure. (14) Rafael Schmidt (Credit Risk Modelling and Estimation via Elliptical Copulae) embeds the concepts of tail dependence and elliptical copulae into the framework of Extreme Value Theory and provides a paramtric and non-paremetric estimator for the tail dependence coefficient. (15) S. Trück and J. Peppel: (Credit Risk Models in Practice -- a Review) provide a valuable survey of the credit risk models in practice, concentrating on four main classes of such models.