Quantile Regression Estimator for GARCH Models


Sangyeol Lee, Department of Statistics, Seoul National University, Seoul, 151-742, Korea. E-mail: sylee@stats.snu.ac.kr


Abstract.  In this article, we study the quantile regression estimator for GARCH models. We formulate the quantile regression problem by a reparametrization method and verify that the obtained quantile regression estimator is strongly consistent and asymptotically normal under certain regularity conditions. We also present our simulation results and a real data analysis for illustration.