A new Pearson-type QMLE for conditionally heteroskedastic models

Ke Zhu and Wai Keung Li

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes a novel Pearson-type quasi maximum likelihood estimator (QMLE) of GARCH($p, q$) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non-Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not the heavy-tailed but also the skewed innovations. Under the stationarity and weak moment conditions, the strong consistency and asymptotical normality of the PQMLE are obtained. With no further efforts, the PQMLE can apply to other conditionally heteroskedastic models. A simulation study is carried out to assess the performance of the PQMLE. Two applications to eight major stock indexes and four exchange rates further highlight the importance of our new method. To our best knowledge, the heavy-tailed and skewed innovations are observed together in practice, and the PQMLE now gives us a systematical way to capture this co-existing feature.

Keywords: Asymmetric innovation; Conditionally heteroskedastic model; Exchange rates; GARCH model; Leptokurtic innovation; Non-Gaussian QMLE; Pearson's Type IV distribution; Pearsonian QMLE; Stock indexes. (search for similar items in EconPapers)
JEL-codes: C1 C13 C58 (search for similar items in EconPapers)
Date: 2013-12-18
New Economics Papers: this item is included in nep-ecm and nep-ore
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Related works:
Working Paper: A new Pearson-type QMLE for conditionally heteroskedastic models (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:52344

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