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B0467
Title: R-estimators in GARCH models: Asymptotics, applications and bootstrapping Authors:  Kanchan Mukherjee - Lancaster University (United Kingdom) [presenting]
Abstract: 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. 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 also consider the weighted bootstrap approximation of the finite sample distributions of the R-estimators. We propose fast algorithms for computing the R-estimators, and their bootstrap replicates. Both real data analysis and simulations show the superior performance of the proposed estimators under the heavy-tailed distributions and the excellent coverage rates of the weighted bootstrap approximations.