Efficient Semi-Parametric Estimation of Non-Gaussian GARCH Processes

26 Pages Posted: 5 Dec 2016 Last revised: 6 Dec 2016

See all articles by Frédéric Godin

Frédéric Godin

Concordia University, Quebec - Department of Mathematics & Statistics; Université Laval

Andrew Luong

Université Laval

Date Written: December 2, 2016

Abstract

Semi-parametric estimators for non-Gaussian GARCH processes based on Feasible Weighted Least Squares (FWLS) are proposed. The estimators are consistent and do not require the specification of the innovations distribution family. The FWLS estimators incorporate information related to the skewness and kurtosis of residuals. This improves their efficiency in comparison to Normal Quasi-Maximum Likelihood (NQML) estimators which only rely on the first two conditional moments. The improved efficiency of FWLS estimators is illustrated in a simulation experiment; the estimation RMSE decreases observed by using the FWLS estimator instead of the NQML range between 0.07% and 10.3% for all parameters of the considered NGARCH with Variance-Gamma innovations. The methodology is also shown to be applicable for the estimation of multivariate GARCH processes.

Keywords: GARCH, Semi-Parametric Estimation, Normal Mean-Variance Mixture, Feasible Weighted Least Squares, Variance-Gamma

JEL Classification: C13, C14, C22

Suggested Citation

Godin, Frédéric and Luong, Andrew, Efficient Semi-Parametric Estimation of Non-Gaussian GARCH Processes (December 2, 2016). Available at SSRN: https://ssrn.com/abstract=2879744 or http://dx.doi.org/10.2139/ssrn.2879744

Frédéric Godin (Contact Author)

Concordia University, Quebec - Department of Mathematics & Statistics ( email )

1455 De Maisonneuve Blvd. W.
Montreal, Quebec H3G 1M8
Canada

Université Laval ( email )

2214 Pavillon J-A. DeSeve
Quebec, Quebec G1K 7P4
Canada

Andrew Luong

Université Laval ( email )

2214 Pavillon J-A. DeSeve
Quebec, Quebec G1K 7P4
Canada

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