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LOCAL ASYMPTOTIC NORMALITY OF GENERAL CONDITIONALLY HETEROSKEDASTIC AND SCORE-DRIVEN TIME-SERIES MODELS

Published online by Cambridge University Press:  21 March 2022

Christian Francq Open the ORCID record for Christian Francq [Opens in a new window]  and
Jean-Michel Zakoian Open the ORCID record for Jean-Michel Zakoian [Opens in a new window]
Christian Francq
Affiliation:
ENSAE-CREST, University of Lille
Jean-Michel Zakoian*
Affiliation:
ENSAE-CREST, University of Lille
*
Address correspondence to Jean-Michel Zakoïan, CREST, 5 Avenue Henri Le Chatelier, 91120 Palaiseau, France; e-mail: Jean-Michel.Zakoian@ensae.fr.
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Abstract

The paper establishes the local asymptotic normality property for general conditionally heteroskedastic time series models of multiplicative form, $\epsilon _t=\sigma _t(\boldsymbol {\theta }_0)\eta _t$, where the volatility $\sigma _t(\boldsymbol {\theta }_0)$ is a parametric function of $\{\epsilon _{s}, s< t\}$, and $(\eta _t)$ is a sequence of i.i.d. random variables with common density $f_{\boldsymbol {\theta }_0}$. In contrast with earlier results, the finite dimensional parameter $\boldsymbol {\theta }_0$ enters in both the volatility and the density specifications. To deal with nondifferentiable functions, we introduce a conditional notion of the familiar quadratic mean differentiability condition which takes into account parameter variation in both the volatility and the errors density. Our results are illustrated on two particular models: the APARCH with asymmetric Student-t distribution, and the Beta-t-GARCH model, and are extended to handle a conditional mean.

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Type
ARTICLES
Information
Econometric Theory , Volume 39 , Issue 5 , October 2023 , pp. 1067 - 1092
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

The authors are grateful to two anonymous referees, the Co-editor, and the Editor for insightful comments, suggestions, and useful criticisms. The authors are grateful to the Agence Nationale de la Recherche (ANR), which supported this work via the Project MLforRisk (ANR-21-CE26-0007) and to the Labex ECODEC.

References

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