Minimum density power divergence estimator for GARCH models

Sangyeol Lee; Junmo Song

doi:10.1007/s11749-008-0093-y

Minimum density power divergence estimator for GARCH models

Sangyeol Lee, Junmo Song

Seoul National University

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

In this paper, we study the robust estimation for the generalized autoregressive conditional heteroscedastic (GARCH) models with Gaussian errors. As a robust estimator, we consider a minimum density power divergence estimator (MDPDE) proposed by Basu et al. (Biometrika 85:549-559, 1998). It is shown that the MDPDE is strongly consistent and asymptotically normal. Our simulation study demonstrates that the MDPDE has robust properties in contrast to the maximum likelihood estimator. A real data analysis is performed for illustration.

Original language	English
Pages (from-to)	316-341
Number of pages	26
Journal	Test
Volume	18
Issue number	2
DOIs	https://doi.org/10.1007/s11749-008-0093-y
State	Published - Aug 2009

Keywords

ARCH models
Asymptotic normality
Consistency
Density-based divergence measures
GARCH models
Robustness

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10.1007/s11749-008-0093-y

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AB - In this paper, we study the robust estimation for the generalized autoregressive conditional heteroscedastic (GARCH) models with Gaussian errors. As a robust estimator, we consider a minimum density power divergence estimator (MDPDE) proposed by Basu et al. (Biometrika 85:549-559, 1998). It is shown that the MDPDE is strongly consistent and asymptotically normal. Our simulation study demonstrates that the MDPDE has robust properties in contrast to the maximum likelihood estimator. A real data analysis is performed for illustration.

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KW - Asymptotic normality

KW - Consistency

KW - Density-based divergence measures

KW - GARCH models

KW - Robustness

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JO - Test

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ER -

Minimum density power divergence estimator for GARCH models

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Keywords

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