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A new Pearson-type QMLE for conditionally heteroskedastic models

  • Zhu, Ke
  • Li, Wai Keung
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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.

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File URL: http://mpra.ub.uni-muenchen.de/52344/1/MPRA_paper_52344.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 52344.

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Date of creation: 18 Dec 2013
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Handle: RePEc:pra:mprapa:52344
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