Nonparametric estimation of conditional VaR and expected shortfall
Nonparametric estimation of conditional VaR and expected shortfall
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DOI：
10.1016/j.jeconom.2008.09.005
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发表时间：
2008-11-01
影响因子：
6.3
通讯作者：
Wang, Xian
中科院分区：
文献类型：
--
作者：
Cai, Zongwu;Wang, Xian
本文考虑了条件风险价值和期望损失函数的一种新的非参数估计。条件风险值的估计是通过反转条件分布函数的加权双重核局部线性估计来实现的。利用插入法构造了条件期望损失的非参数估计。在时间序列数据的边界点和内部点上，建立了所提出的非参数估计的渐近正态性和相合性。我们证明了加权双核局部线性条件分布估计除了具有双核局部线性估计和加权Nadaraya-Watson估计的良好性质外，还具有始终为分布、连续和可微的优点。此外，基于Akaike信息准则的非参数版本，提出了一种adhoc数据驱动的时尚带宽选择方法。最后，进行了实证研究，以说明所提出的估计的有限样本性能。由爱思唯尔公司出版
This paper considers a new nonparametric estimation of conditional Value-at-risk and expected shortfall functions. Conditional value-at-risk is estimated by inverting the weighted double kernel local linear estimate of the conditional distribution function. The nonparametric estimator of conditional expected shortfall is constructed by a plugging-in method. Both the asymptotic normality and consistency of the proposed nonparametric estimators are established at both boundary and interior points for time series data. We show that the weighted double kernel local linear conditional distribution estimator has the advantages of always being a distribution, continuous, and differentiable, besides the good properties from both the double kernel local linear and weighted Nadaraya-Watson estimators, Moreover, an ad hoc data-driven fashion bandwidth selection method is proposed, based on the nonparametric version of the Akaike information criterion. Finally, an empirical Study is carried out to illustrate the finite sample performance of the proposed estimators. Published by Elsevier B.V.