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A new Pearson-type QMLE for conditionally heteroskedastic models

Zhu, Ke and Li, Wai Keung (2014): A new Pearson-type QMLE for conditionally heteroskedastic models.

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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 just the heavy-tailed but also the skewed innovations. Under strict stationarity and some 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. Heavy-tailed and skewed innovations are often observed together in practice, and the PQMLE now gives us a systematical way to capture these two co-existing features.

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