Nonlinear expectile regression with application to Value-at-Risk and expected shortfall estimation

被引：43
|
作者：
Kim, Minjo [1 ]
Lee, Sangyeol [1 ]
机构：
[1] Seoul Natl Univ, Dept Stat, Seoul 151747, South Korea
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2016年 / 94卷
基金：
新加坡国家研究基金会;
关键词：
Expectile regression; Expected shortfall; Value-at-Risk; Asymmetric least squares regression; Consistency; Asymptotic normality; MAXIMUM-LIKELIHOOD-ESTIMATION; GARCH; VOLATILITY;
D O I：
10.1016/j.csda.2015.07.011
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper considers nonlinear expectile regression models to estimate conditional expected shortfall (ES) and Value-at-Risk (VaR). In the literature, the asymmetric least squares (ALS) regression method has been widely used to estimate expectile regression models. However, no literatures rigorously investigated the asymptotic properties of the ALS estimates in nonlinear models with heteroscedasticity. Motivated by this aspect, this paper studies the consistency and asymptotic normality of the ALS estimates and conditional VaR and ES in those models. To illustrate, a simulation study and real data analysis are conducted. (C) 2015 Elsevier B.V. All rights reserved.
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页码：1 / 19
页数：19
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