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Bootstrapping Stationary ARMA-GARCH Models by Kenichi Shimizu is a nonfiction available to read on EtoBox.

What is Bootstrapping Stationary ARMA-GARCH Models about?

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.

Who reads Bootstrapping Stationary ARMA-GARCH Models?

Author

Kenichi Shimizu

Publisher

Vieweg+Teubner Verlag / Springer Fachmedien Wiesbaden GmbH, Wiesbaden

Published

2010

Language

EN

ISBN

9783834809926

Category

nonfiction

Subjects

Mathematics, Stem