Ingeniero

Measuring Risk in Complex Stochastic Systems
J. Franke, W. H¨rdle, G. Stahl a

Empirical Volatility

Parameter Estimates

http://www.xplore-stat.de/ebooks/ebooks.html

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Preface
Complex dynamic processes of life and sciences generate risks that have to be taken. The need for clear and distinctive definitions of different kinds of risks, adequate methods and parsimonious models isobvious. The identification of important risk factors and the quantification of risk stemming from an interplay between many risk factors is a prerequisite for mastering the challenges of risk perception, analysis and management successfully. The increasing complexity of stochastic systems, especially in finance, have catalysed the use of advanced statistical methods for these tasks. The methodologicalapproach to solving risk management tasks may, however, be undertaken from many different angles. A financial institution may focus on the risk created by the use of options and other derivatives in global financial processing, an auditor will try to evaluate internal risk management models in detail, a mathematician may be interested in analysing the involved nonlinearities or concentrate on extremeand rare events of a complex stochastic system, whereas a statistician may be interested in model and variable selection, practical implementations and parsimonious modelling. An economist may think about the possible impact of risk management tools in the framework of efficient regulation of financial markets or efficient allocation of capital. This book gives a diversified portfolio of thesescenarios. We first present a set of papers on credit risk management, and then focus on extreme value analysis. The Value at Risk (VaR) concept is discussed in the next block of papers, followed by several articles on change points. The papers were presented during a conference on Measuring Risk in Complex Stochastic Systems that took place in Berlin on September 25th - 30th 1999. The conference wasorganised within the Seminar Berlin-Paris, Seminaire Paris-Berlin. The paper by Lehrbass considers country risk within a no-arbitrage model and combines it with the extended Vasicek term structure model and applies the developed theory to DEM- Eurobonds. Kiesel, Perraudin and Taylor construct a model free volatility estimator to investigate the long horizon volatility of various short term interestrates. Hanousek investigates the failing of Czech banks during the early nineties. M¨ller and u Rnz apply a Generalized Partial Linear Model to evaluating credit risk based on a credit scoring data set from a French bank. Overbeck considers the problem of capital allocation in the framework of credit risk and loan portfolios. The analysis of extreme values starts with a paper by Novak, who considersconfidence intervals for tail index estimators. Robert presents a novel approach to extreme value

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calculation on state of the art α-ARCH models. Kleinow and Thomas show how in a client/server architecture the computation of extreme value parameters may be undertaken with the help of WWW browsers and an XploRe Quantlet Server. The VaR section starts with Cumperayot, Danielsson and deVrieswho discuss basic questions of VaR modelling and focus in particular on economic justifications for external and internal risk management procedures and put into question the rationale behind VaR. Slaby and Kokoschka deal with with change-points. Slaby considers methods based on ranks in an iid framework to detect shifts in location, whereas Kokoszka reviews CUSUM-type esting and estimatingprocedures for the change-point problem in ARCH models. Huschens and Kim concentrate on the stylised fact of heavy tailed marginal distributions for financial returns time series. They model the distributions by the family of α-stable laws and consider the consequences for β values in the often applied CAPM framework. Breckling, Eberlein and Kokic introduce the generalised hyperbolic model to calculate...