论文标题
非参数预期不足的预测，结合了加权分位数
Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles
论文作者
论文摘要
提出了一个新的半参数预期不足（ES）估计和预测框架。提出的方法基于两步估计程序。第一步涉及通过一组分位数时间序列回归在不同分位数水平下对价值风险（VAR）的估计。然后，将ES计算为估计分位数的加权平均值。分位数的加权结构通过β权重函数而隔离地参数化，其系数可通过最大程度地减少Fissler-Ziegel类的关节量和ES损耗函数来优化。首先使用两个数据生成过程进行广泛的模拟研究，首先评估所提出方法的属性。然后进行了两项具有不同样本大小不同的预测研究，其中一项侧重于2008年全球金融危机（GFC）时期。提出的模型应用于7个股票市场指数，并将其预测性能与一系列参数，非参数和半参数模型进行了比较，包括GARCH，有条件的自动回归期望（CARE），关节VAR和ES分位数回归模型以及单位数量的简单平均值。预测实验的结果为支持拟议模型提供了明确的证据。
A new semi-parametric Expected Shortfall (ES) estimation and forecasting framework is proposed. The proposed approach is based on a two-step estimation procedure. The first step involves the estimation of Value-at-Risk (VaR) at different quantile levels through a set of quantile time series regressions. Then, the ES is computed as a weighted average of the estimated quantiles. The quantiles weighting structure is parsimoniously parameterized by means of a Beta weight function whose coefficients are optimized by minimizing a joint VaR and ES loss function of the Fissler-Ziegel class. The properties of the proposed approach are first evaluated with an extensive simulation study using two data generating processes. Two forecasting studies with different out-of-sample sizes are then conducted, one of which focuses on the 2008 Global Financial Crisis (GFC) period. The proposed models are applied to 7 stock market indices and their forecasting performances are compared to those of a range of parametric, non-parametric and semi-parametric models, including GARCH, Conditional AutoRegressive Expectile (CARE), joint VaR and ES quantile regression models and simple average of quantiles. The results of the forecasting experiments provide clear evidence in support of proposed models.
