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

By K. Zhu, S. Ling. 2011

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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.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ba92f2dea25923ffbe74d22f4c7c8f7803622ecc

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Citations in the corpus

Risk-parameter estimation in volatility models (2012, methodology): “Estimation of conditional volatility models under moment conditions different from (3) has been studied by Berkes and Horváth (2004), Zhu and Ling (2011), Francq and Zakoian (2012) using nonGaussian QML estimators.”
LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises (2015, methodology): “The technique we used for LADE and SLADE in this article is from Zhu and Ling (2011, 2012, 2013).”