Robust Estimation and Inference for Heavy Tailed Nonlinear GARCH

37 Pages Posted: 12 Jan 2012

See all articles by Jonathan B. Hill

Jonathan B. Hill

University of North Carolina (UNC) at Chapel Hill – Department of Economics

Date Written: January 10, 2012

Abstract

We develop new tail-trimmed QML estimators for nonlinear GARCH models with possibly heavy tailed errors. Tail-trimming allows both identification of the true parameter and asymptotic normality. In heavy tailed cases the rate of convergence is below but arbitrarily close to root-n, the highest possible amongst M-estimators for GARCH with errors that have an infinite fourth moment, and faster than QML. We present a consistent estimator of the covariance matrix that permits classic inference without knowledge of the rate of convergence. Finally, a simulation study shows our estimators trump existing ones for sharpness and approximate normality, and we apply them to financial returns data.

Suggested Citation

Hill, Jonathan B., Robust Estimation and Inference for Heavy Tailed Nonlinear GARCH (January 10, 2012). Available at SSRN: https://ssrn.com/abstract=1982925 or http://dx.doi.org/10.2139/ssrn.1982925

Jonathan B. Hill (Contact Author)

University of North Carolina (UNC) at Chapel Hill – Department of Economics ( email )

102 Ridge Road
Chapel Hill, NC NC 27514
United States

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