Quantile Regression Estimator for GARCH Models

Authors


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

Abstract

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.

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