Skip to main content
SpringerLink
Log in

Measuring Risk in Complex Stochastic Systems

  • Book
  • © 2000

Overview

Editors:
  1. Jürgen Franke

    1. Fachbereich Mathematik, Universität Kaiserslautern, Kaiserslautern, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Gerhard Stahl

    1. Bundesaufsichtsamt für das Kreditwesen, Berlin, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Wolfgang Härdle

    1. Wirschaftswissenschaftliche Fakultät, Institut für Statistik und Okonometrie Humboldt-Universität zu Berlin, Berlin, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Statistics (LNS, volume 147)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (15 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Allocation of Economic Capital in loan portfolios

    • Ludger Overbeck
    Pages 1-17

  3. Estimating Volatility for Long Holding Periods

    • Rüdiger Kiesel, William Perraudin, Alex Taylor
    Pages 19-31

  4. A Simple Approach to Country Risk

    • Frank Lehrbass
    Pages 33-67

  5. Predicting Bank Failures in Transition: Lessons from the Czech Bank Crisis of the mid-Nineties

    • Jan Hanousek
    Pages 69-81

  6. Credit Scoring using Semiparametric Methods

    • Marlene Müller, Bernd Rönz
    Pages 83-97

  7. On the (Ir)Relevancy of Value-at-Risk Regulation

    • Phornchanok J. Cumperayot, Jon Danielsson, Bjorn N. Jorgensen, Caspar G. de Vries
    Pages 99-117

  8. Backtesting beyond VaR

    • Wolfgang Härdle, Gerhard Stahl
    Pages 119-130

  9. Measuring Implied Volatility Surface Risk using Principal Components Analysis

    • Alpha Sylla, Christophe Villa
    Pages 131-148

  10. Detection and estimation of changes in ARCH processes

    • Piotr Kokoszka, Remigijus Leipus
    Pages 149-160

  11. Behaviour of Some Rank Statistics for Detecting Changes

    • Aleš Slabý
    Pages 161-174

  12. A stable CAPM in the presence of heavy-tailed distributions

    • Stefan Huschens, Jeong-Ryeol Kim
    Pages 175-188

  13. A Tailored Suit for Risk Management: Hyperbolic Model

    • Jens Breckling, Ernst Eberlein, Philip Kokic
    Pages 189-202

  14. Computational Resources for Extremes

    • Torsten Kleinow, Michael Thomas
    Pages 203-213

  15. Confidence intervals for a tail index estimator

    • Sergei Y. Novak
    Pages 215-222

  16. Extremes of alpha-ARCH Models

    • Christian Robert
    Pages 223-257

  17. Back Matter

    Pages 259-260
    Download chapter PDF
Back to top

Keywords

About this book

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 is obvious. 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 methodological approach to solving risk management tasks may, however, be undertaken from many different angles. A financial insti­ tution may focus on the risk created by the use of options and other derivatives in global financial processing, an auditor will try to evalu­ ate internal risk management models in detail, a mathematician may be interested in analysing the involved nonlinearities or concentrate on extreme and rare events of a complex stochastic system, whereas a statis­ tician may be interested in model and variable selection, practical im­ plementations 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.

Editors and Affiliations

  • Fachbereich Mathematik, Universität Kaiserslautern, Kaiserslautern, Germany

    Jürgen Franke

  • Bundesaufsichtsamt für das Kreditwesen, Berlin, Germany

    Gerhard Stahl

  • Wirschaftswissenschaftliche Fakultät, Institut für Statistik und Okonometrie Humboldt-Universität zu Berlin, Berlin, Germany

    Wolfgang Härdle

Bibliographic Information

  • Book Title: Measuring Risk in Complex Stochastic Systems

  • Editors: Jürgen Franke, Gerhard Stahl, Wolfgang Härdle

  • Series Title: Lecture Notes in Statistics

  • DOI: https://doi.org/10.1007/978-1-4612-1214-0

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 2000

  • Softcover ISBN: 978-0-387-98996-9Published: 15 June 2000

  • eBook ISBN: 978-1-4612-1214-0Published: 06 December 2012

  • Series ISSN: 0930-0325

  • Series E-ISSN: 2197-7186

  • Edition Number: 1

  • Number of Pages: XIV, 260

  • Number of Illustrations: 3 b/w illustrations

  • Topics: Statistics, general, Quantitative Finance

Publish with us

Policies and ethics

Back to top

Search

Navigation