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Bootstrapping Stationary ARMA-GARCH Models
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Wiesbaden :
Vieweg+Teubner Verlag : Imprint: Vieweg+Teubner Verlag,
2010.
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| Edición: | 1st ed. 2010. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 020 | |a 9783834897787 |9 978-3-8348-9778-7 | ||
| 024 | 7 | |a 10.1007/978-3-8348-9778-7 |2 doi | |
| 050 | 4 | |a QA273.A1-274.9 | |
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| 100 | 1 | |a Shimizu, Kenichi. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Bootstrapping Stationary ARMA-GARCH Models |h [electronic resource] / |c by Kenichi Shimizu. |
| 250 | |a 1st ed. 2010. | ||
| 264 | 1 | |a Wiesbaden : |b Vieweg+Teubner Verlag : |b Imprint: Vieweg+Teubner Verlag, |c 2010. | |
| 300 | |a 148 p. 12 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Probabilities. | |
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Mathematics. | |
| 650 | 1 | 4 | |a Probability Theory. |
| 650 | 2 | 4 | |a Mathematical Modeling and Industrial Mathematics. |
| 650 | 2 | 4 | |a Mathematics. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783834809926 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-8348-9778-7 |z Texto Completo |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||