Quantile Regression Estimator for GARCH Models

被引:28
|
作者
Lee, Sangyeol [1 ]
Noh, Jungsik [1 ]
机构
[1] Seoul Natl Univ, Dept Stat, Seoul 151742, South Korea
关键词
argmin sequence; asymptotic normality; bracketing method; GARCH models; non-convex optimization; quantile regression; reparametrization method; strong consistency; value at risk; MAXIMUM LIKELIHOOD ESTIMATOR; AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY; ABSOLUTE DEVIATIONS ESTIMATION; INFINITE VARIANCE; ARCH MODELS; TIME-SERIES; INFERENCE; ERRORS; HETEROSKEDASTICITY; CONSISTENCY;
D O I
10.1111/j.1467-9469.2011.00759.x
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
. 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.
引用
收藏
页码:2 / 20
页数:19
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