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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -