英文标题：
《A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall
Regression Framework》
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作者：
Chao Wang, Richard Gerlach, Qian Chen
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最新提交年份：
2021
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英文摘要：
A new realized conditional autoregressive Value-at-Risk (VaR) framework is proposed, through incorporating a measurement equation into the original quantile regression model. The framework is further extended by employing various Expected Shortfall (ES) components, to jointly estimate and forecast VaR and ES. The measurement equation models the contemporaneous dependence between the realized measure (i.e., Realized Variance and Realized Range) and the latent conditional ES. An adaptive Bayesian Markov Chain Monte Carlo method is employed for estimation and forecasting, the properties of which are assessed and compared with maximum likelihood through a simulation study. In a comprehensive forecasting study on 1% and 2.5 % quantile levels, the proposed models are compared to a range of parametric, non-parametric and semi-parametric models, based on 7 market indices and 7 individual assets. One-day-ahead VaR and ES forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
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中文摘要：
通过在原始分位数回归模型中加入度量方程，提出了一种新的实现条件自回归风险值（VaR）框架。该框架通过采用各种预期缺口（ES）组件进一步扩展，以联合估计和预测VaR和ES。测量方程建模了已实现测量（即已实现方差和已实现范围）与潜在条件ES之间的同期相关性。采用自适应贝叶斯马尔可夫链蒙特卡罗方法进行估计和预测，并通过仿真研究对其性能进行了评估，并与最大似然法进行了比较。在1%和2.5%分位数水平的综合预测研究中，基于7个市场指数和7个单项资产，将拟议模型与一系列参数、非参数和半参数模型进行了比较。提前一天的VaR和ES预测结果有利于所提出的模型，尤其是在模型中加入次抽样实现方差和次抽样实现范围时。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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