Nonlinear expectile regression with application to Value-at-Risk and expected shortfall estimation
Nonlinear expectile regression with application to Value-at-Risk and expected shortfall estimation
复制标题
DOI：
10.1016/j.csda.2015.07.011
复制
发表时间：
2016-02-01
影响因子：
1.8
通讯作者：
Lee, Sangyeol
中科院分区：
文献类型：
--
作者：
Kim, Minjo;Lee, Sangyeol
本文考虑非线性期望回归模型估计条件期望亏损和风险价值。在文献中，非对称最小二乘（ALS）回归方法已被广泛应用于期望回归模型的估计。然而，没有文献严格研究异方差非线性模型中ALS估计的渐近性质。基于这方面的考虑，本文研究了这类模型中ALS估计、条件VaR和ES的相合性和渐近正态性。为了说明，进行了模拟研究和真实的数据分析。(C)2015 Elsevier B.V.版权所有。
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.