Shimizu Bootstrapping Stationary ARMA-GARCH Models

1. Auflage 2010
ISBN: 978-3-8348-9778-7
Verlag: Vieweg & Teubner
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 137 Seiten, Web PDF

Reihe: Mathematics and Statistics

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 the 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. Together with the theoretical investigation simulation studies from the application of the proposed bootstrap methods are demonstrated.

Zielgruppe

Research

Autoren/Hrsg.

Shimizu, Kenichi

Inhaltsverzeichnis

Bootstrap Does not Always Work.- Parametric AR(p)-ARCH(q) Models.- Parametric ARMA(p, q)- GARCH(r, s) Models.- Semiparametric AR(p)-ARCH(1) Models.

Über Autor(innen)

Dr. Kenichi Shimizu completed his doctoral thesis at the Department of Mathematics at the Technical University, Braunschweig.