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Measuring Risk in Complex Stochastic Systems

  • Jürgen Franke
  • Gerhard Stahl
  • Wolfgang Härdle

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

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Rüdiger Kiesel, William Perraudin, Alex Taylor
    Pages 19-31
  3. Frank Lehrbass
    Pages 33-67
  4. Marlene Müller, Bernd Rönz
    Pages 83-97
  5. Phornchanok J. Cumperayot, Jon Danielsson, Bjorn N. Jorgensen, Caspar G. de Vries
    Pages 99-117
  6. Wolfgang Härdle, Gerhard Stahl
    Pages 119-130
  7. Piotr Kokoszka, Remigijus Leipus
    Pages 149-160
  8. Stefan Huschens, Jeong-Ryeol Kim
    Pages 175-188
  9. Jens Breckling, Ernst Eberlein, Philip Kokic
    Pages 189-202
  10. Torsten Kleinow, Michael Thomas
    Pages 203-213
  11. Sergei Y. Novak
    Pages 215-222
  12. Christian Robert
    Pages 223-257
  13. Back Matter
    Pages 259-260

About this book

Introduction

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.

Keywords

Estimator Projection pursuit Semiparametric Model linear optimization statistics

Editors and affiliations

  • Jürgen Franke
    • 1
  • Gerhard Stahl
    • 2
  • Wolfgang Härdle
    • 3
  1. 1.Fachbereich MathematikUniversität KaiserslauternKaiserslauternGermany
  2. 2.Bundesaufsichtsamt für das KreditwesenBerlinGermany
  3. 3.Wirschaftswissenschaftliche FakultätInstitut für Statistik und Okonometrie Humboldt-Universität zu BerlinBerlinGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-1214-0
  • Copyright Information Springer-Verlag New York, Inc. 2000
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-98996-9
  • Online ISBN 978-1-4612-1214-0
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site
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