Bootstrapping Stationary ARMA-GARCH Models

  • Authors
  • Kenichi Shimizu

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Kenichi Shimizu
    Pages 1-7
  3. Kenichi Shimizu
    Pages 9-17
  4. Kenichi Shimizu
    Pages 19-64
  5. Kenichi Shimizu
    Pages 65-83
  6. Kenichi Shimizu
    Pages 85-126
  7. Back Matter
    Pages 121-131

About this book

Introduction

Bootstrap technique is a useful tool for assessing uncertainty in statistical estimation and thus it is widely applied for risk management. Bootstrap is without doubt a promising technique, however, it is not applicable to all time series models. A wrong application could lead to a false decision to take too much risk.

Kenichi Shimizu investigates the limit of the two standard bootstrap techniques, the residual and the wild bootstrap, when these are applied to the conditionally heteroscedastic models, such as the ARCH and GARCH models. The author shows that the wild bootstrap usually does not work well when one estimates conditional heteroscedasticity of Engle’s ARCH or Bollerslev’s GARCH models while the residual bootstrap works without problems. Simulation studies from the application of the proposed bootstrap methods are demonstrated together with the theoretical investigation.

Keywords

Bootstrapping RM Risk modelling Time series bootstrap methods conditionally heteroscedastic models mathematical statistics risk management time series analysis

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-8348-9778-7
  • Copyright Information Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2010
  • Publisher Name Vieweg+Teubner
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-8348-0992-6
  • Online ISBN 978-3-8348-9778-7
  • About this book
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