Simple GMM Estimation of the Semi-Strong GARCH(1,1) Model

Todd Prono ()

MPRA Paper from University Library of Munich, Germany

Abstract: Efficient GMM estimation of the semi-strong GARCH(1,1) model requires simultaneous estimation of the conditional third and fourth moments. This paper proposes a simple alternative to efficient GMM based upon the unconditional skewness of residuals and the autocovariances of squared residuals. An advantage of this simple alternative is that neither the third nor the fourth conditional moment needs to be estimated. A second advantage is that linear estimators apply to all of the parameters in the model, making estimation straightforward in practice. The proposed estimators are IV-like with potentially many instruments. Sequential estimation involves TSLS in a first step followed by linear GMM. Simultaneous estimation involves either two-step GMM or CUE. A Monte Carlo study of the proposed estimators is included.

Keywords: GARCH; Time Series Heteroskedasticity; GMM; CUE; Many Moments; Conditional Moment Restrictions; Consistency; Robust Statistics (search for similar items in EconPapers)
JEL-codes: C22 C53 G12 (search for similar items in EconPapers)
Date: 2010-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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https://mpra.ub.uni-muenchen.de/20034/1/MPRA_paper_20034.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/26190/1/MPRA_paper_26190.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/27532/1/MPRA_paper_27532.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/28540/1/MPRA_paper_28540.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/33634/1/MPRA_paper_33634.pdf revised version (application/pdf)

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