Inference in ARCH and GARCH models with heavy-tailed errors

Hall, Peter; Yao, Qiwei

Inference in ARCH and GARCH models with heavy-tailed errors

Date

2003

Authors

Hall, Peter

Yao, Qiwei

Publisher

Blackwell Publishing Ltd

Abstract

ARCH and GARCH models directly address the dependency of conditional second moments, and have proved particularly valuable in modelling processes where a relatively large degree of fluctuation is present. These include financial time series, which can be

Keywords

Keywords: Approximation theory; Finance; Mathematical models; Maximum likelihood estimation; Parameter estimation; Garch models; Inference; Time series analysis Autoregression; Bootstrap; Dependent data; Domain at attraction; Financial data; Limit theory; Percentile-t bootstrap; Quasi-maximum likelihood; Semiparametric inference; Stable law; Studentize; Subsample bootstrap; Time series

URI

http://hdl.handle.net/1885/74623

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ANU Research Publications

Source

Econometrica

Type

Journal article

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Inference in ARCH and GARCH models with heavy-tailed errors

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