Weighted quantile regression estimation of GARCH models

Articles

Weighted quantile regression estimation of GARCH models [CET 2014]
Author(s)
Xuejun Jang
ABSTRACT
In modelling volatility in financial time series, the generalized autoregressive conditional heteroscedastic (GARCH) model has achieved great success and been widely applied. In this article, we study the weighted quantile regression estimator for GARCH models. This method involves a sequence of weights and takes a data-driven weighting scheme to maximize the asymptotic efficiency of the estimators. Under regularity conditions, we establish asymptotic distributions of the proposed estimators for a variety of heavy- or light-tailed error distributions including the normal, mixed-normal, Student's t, Cauchy distributions, etc.. We also present our simulation results and a real data analysis for illustration.
KEYWORDS
Weighted quantile regression, GARCH models
References