On the Efficiency of Conditional Heteroskedasticity Models

Review of Quantitative Finance and Accounting, 10 (1998): 21-37

Posted: 31 Mar 2013

See all articles by T. Lee

T. Lee

Independent

Tony S. Wirjanto

University of Waterloo - School of Accounting and Finance; University of Waterloo, Department of Statistics & Actuarial Science

Date Written: 1998

Abstract

This paper discusses how conditional heteroskedasticity models can be estimated efficiently without imposing strong distributional assumptions such as normality. Using the generalized method of moments (GMM) principle, we show that for a class of models with a symmetric conditional distribution, the GMM estimates obtained from the joint estimating equations corresponding to the conditional mean and variance of the model are efficient when the instruments are chosen optimally. A simple ARCH(1) model is used to illustrate the feasibility of the proposed estimation procedure.

Keywords: Financial time series, ARCH, non-normality, generalized method of moments, optimal choice of instruments, maximum likelihood, efficiency

Suggested Citation

Lee, T. and Wirjanto, Tony S., On the Efficiency of Conditional Heteroskedasticity Models (1998). Review of Quantitative Finance and Accounting, 10 (1998): 21-37, Available at SSRN: https://ssrn.com/abstract=2241884

T. Lee

Independent ( email )

Tony S. Wirjanto (Contact Author)

University of Waterloo - School of Accounting and Finance ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1
Canada
519-888-4567 x35210 (Phone)

HOME PAGE: http://https://uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

University of Waterloo, Department of Statistics & Actuarial Science ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1
Canada
519-888-4567 x35210 (Phone)
519-746-1875 (Fax)

HOME PAGE: http://math.uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

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