TY - JOUR

T1 - R-estimators in GARCH models

T2 - asymptotics and applications

AU - Liu, Hang

AU - Mukherjee, Kanchan

PY - 2021/12/31

Y1 - 2021/12/31

N2 - The quasi-maximum likelihood estimation is a commonly-used method for estimating the GARCH parameters. However, such estimators are sensitive to outliers and their asymptotic normality is proved under the finite fourth moment assumption on the underlying error distribution. In this paper, we propose a novel class of estimators of the GARCH parameters based on ranks of the residuals, called R-estimators, with the property that they are asymptotically normal under the existence of a finite $2+\delta$ moment of the errors and are highly efficient. We propose fast algorithm for computing the R-estimators.Both real data analysis and simulations show the superior performance of theproposed estimators under the heavy-tailed and asymmetric distributions.

AB - The quasi-maximum likelihood estimation is a commonly-used method for estimating the GARCH parameters. However, such estimators are sensitive to outliers and their asymptotic normality is proved under the finite fourth moment assumption on the underlying error distribution. In this paper, we propose a novel class of estimators of the GARCH parameters based on ranks of the residuals, called R-estimators, with the property that they are asymptotically normal under the existence of a finite $2+\delta$ moment of the errors and are highly efficient. We propose fast algorithm for computing the R-estimators.Both real data analysis and simulations show the superior performance of theproposed estimators under the heavy-tailed and asymmetric distributions.

KW - R-estimation

KW - Empirical process

KW - GARCH models

U2 - 10.1093/ectj/utab026

DO - 10.1093/ectj/utab026

M3 - Journal article

VL - 25

SP - 98

EP - 113

JO - The Econometrics Journal

JF - The Econometrics Journal

SN - 1368-4221

IS - 1

ER -