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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Bootstrapping Stationary Arma-Garch Models

Bootstrapping Stationary Arma-Garch Models

Shimizu, Kenichi

Paperback

General MathematicsProbability & Statistics

ISBN10: 3834809926
ISBN13: 9783834809926
Publisher: Vieweg+teubner Verlag
Published: Jan 27 2010
Pages: 148
Weight: 0.40
Height: 0.40 Width: 5.80 Depth: 8.20
Language: English
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.

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