Closed-Form Estimation of Finite-Order ARCH Models: Asymptotic Theory and Finite-Sample Performance

44 Pages Posted: 10 Sep 2014 Last revised: 9 Feb 2018

Multiple version iconThere are 2 versions of this paper

Date Written: February 06, 2018

Abstract

Strong consistency and (weak) distributional convergence to highly non-Gaussian limits are established for closed-form, two stage least squares (TSLS) estimators for a class of ARCH(p) models, with special attention paid to the ARCH(1) and Threshold ARCH(1) cases. Conditions for these results include (relatively) mild moment existence criteria that enjoy empirical support among many financial returns. These conditions are not shared by competing estimators like OLS. Identification of the TSLS estimators depends on asymmetry, either in the model's rescaled errors or in the conditional variance function. Monte Carlo studies reveal TSLS estimation can sizably outperform quasi maximum likelihood (QML) and compare favorably to recently proposed two step estimators designed to enhance the efficiency of QML.

Keywords: ARCH, Threshold ARCH, closed form, two stage least squares, instrumental variables, heavy tails, regular variation

JEL Classification: C13, C22, C58

Suggested Citation

Prono, Todd, Closed-Form Estimation of Finite-Order ARCH Models: Asymptotic Theory and Finite-Sample Performance (February 06, 2018). Available at SSRN: https://ssrn.com/abstract=2493394 or http://dx.doi.org/10.2139/ssrn.2493394

Todd Prono (Contact Author)

Federal Reserve Board ( email )

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

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