Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA-GARCH/IGARCH models

Ke Zhu () and Shiqing Ling ()

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates the asymptotic theory of the quasi-maximum exponential likelihood estimators (QMELE) for ARMA–GARCH models. Under only a fractional moment condition, the strong consistency and the asymptotic normality of the global self-weighted QMELE are obtained. Based on this self-weighted QMELE, the local QMELE is showed to be asymptotically normal for the ARMA model with GARCH (finite variance) and IGARCH errors. A formal comparison of two estimators is given for some cases. A simulation study is carried out to assess the performance of these estimators, and a real example on the world crude oil price is given.

Keywords: ARMA–GARCH/IGARCH model; asymptotic normality; global selfweighted/local quasi-maximum exponential likelihood estimator; strong consistency. (search for similar items in EconPapers)
JEL-codes: C13 C5 (search for similar items in EconPapers)
Date: 2013-11-17
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (1)

Published in Annals of Statistics 4.39(2011): pp. 2131-2163

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