Robust and efficient estimation with weighted composite quantile regression

被引:8
作者
Jiang, Xuejun [1 ]
Li, Jingzhi [1 ]
Xia, Tian [2 ]
Yan, Wanfeng [1 ,3 ]
机构
[1] Southern Univ Sci & Technol, Dept Math, Shenzhen 518055, Peoples R China
[2] Guizhou Univ Finance & Econ, Guiyang 550025, Peoples R China
[3] Banque Pictet & Cie SA, Route Acacias 60, CH-1211 Geneva 73, Switzerland
关键词
Weighted CQR; Oracle MLE; Extended interior algorithm; Double threshold ARCH models; ALGORITHM; ARCH;
D O I
10.1016/j.physa.2016.03.056
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed normal, Student's t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:413 / 423
页数:11
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