Bootstrapping tests for conditional heteroskedasticity based on artificial neural network

Author

De Peretti, Christian ; Siani, Carole

Author_Institution

Dept. of Econ., Univ. of Evry-Val-d´´Essonne

Volume

1

fYear

2006

fDate

4-6 Oct. 2006

Firstpage

372

Lastpage

379

Abstract

This paper deals with bootstrapping tests, based on the LM statistic and on a neural statistic, for detecting conditional heteroskedasticity in the context of standard and non-standard ARCH models. Although the tests of the literature are asymptotically valid, they are not exact in finite samples, and suffer from a substantial size distortion, and has to be accounted for. In this paper, we propose to solve this problem using parametric and nonparametric bootstrap methods, based on simulation techniques, making it possible to obtain a better finite-sample estimate of the test statistic distribution than the asymptotic distribution

Keywords

autoregressive processes; bootstrapping; estimation theory; neural nets; statistical distributions; statistical testing; ARCH models; LM statistic; artificial neural network; asymptotic distribution; bootstrapping tests; conditional heteroskedasticity; finite-sample estimate; neural statistic; nonparametric bootstrap methods; parametric bootstrap methods; simulation techniques; statistic distribution; Artificial neural networks; Context modeling; Lagrangian functions; Monte Carlo methods; Nonlinear distortion; Parametric statistics; Statistical analysis; Statistical distributions; Systems engineering and theory; Testing; ARCH models; Bootstrap tests; artificial neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Engineering in Systems Applications, IMACS Multiconference on

Conference_Location

Beijing

Print_ISBN

7-302-13922-9

Electronic_ISBN

7-900718-14-1

Type

conf

DOI

10.1109/CESA.2006.4281681

Filename

4281681