Simple Estimators for the GARCH(1,1) Model

52 Pages Posted: 24 Nov 2009 Last revised: 9 Sep 2014

Date Written: July 2014

Abstract

I propose closed-form estimators for the GARCH(1,1) model that are based on second-order covariances. The ability to obtain closed-form estimates derives from skewness in the sequence being modeled, which permits separate identification and estimation of the ARCH and GARCH effects. I show these estimators to be CAN under weak stationarity using Martingale limit theory. I also demonstrate conditions under which an iterative GLS estimator reliant on these closed-form estimates as starting values shares the same asymptotic distribution with the QMLE. This asymptotic equivalence is achieved given only third moment existence, which substantially relaxes the moment existence criteria generally required for OLS- and TSLS-style estimators of GARCH processes. The proposed estimators are studied in Monte Carlo experiments and applied to a suite of financial data.

Keywords: GARCH, GMM, closed-form, many moments, skewness

JEL Classification: C13, C22, C53

Suggested Citation

Prono, Todd, Simple Estimators for the GARCH(1,1) Model (July 2014). Available at SSRN: https://ssrn.com/abstract=1511720 or http://dx.doi.org/10.2139/ssrn.1511720

Todd Prono (Contact Author)

Federal Reserve Board ( email )

20th and Constitution Ave NW
Washington, DC 20551
United States

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