α-混合序列下期望损失ES的两步核估计

Two-Step Kernel Estimation of Expected Shortfall for Strong Mixing Time Series

  • 摘要: 期望损失(Expected Shortfall, ES)是当今最流行的金融资产风险管理的工具之一, 是一个理想的一致性风险度量. 本文在\alpha-混合序列具有幂衰减混合系数条件下, 用两步核估计估算风险度量ES的值, 第一步是在险价值(Value at Risk, VaR)的核估计, 第二步是ES的核估计. 得到ES的核估计量的\, Bahadur表示, 以及均方误差和渐近正态性的收敛速度.

     

    Abstract: Expected Shortfall (ES) is one of the most popular tools of risk management for financial property, and is an ideal coherent risk measure. In this paper, we discuss the two-step kernel estimator of ES under polynomial decay of strong mixing coefficients of time series. The first step is the kernel estimator of VaR (Value at Risk) and the second step is the kernel estimator of ES. We obtain Bahadur representation of the kernel estimator of ES. Then, we give the mean squares error and the rate of the asymptotic normality.

     

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