英文标题：
《Improving Value-at-Risk prediction under model uncertainty》
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作者：
Shige Peng, Shuzhen Yang and Jianfeng Yao
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最新提交年份：
2020
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英文摘要：
Several well-established benchmark predictors exist for Value-at-Risk (VaR), a major instrument for financial risk management. Hybrid methods combining AR-GARCH filtering with skewed-$t$ residuals and the extreme value theory-based approach are particularly recommended. This study introduces yet another VaR predictor, G-VaR, which follows a novel methodology. Inspired by the recent mathematical theory of sublinear expectation, G-VaR is built upon the concept of model uncertainty, which in the present case signifies that the inherent volatility of financial returns cannot be characterized by a single distribution but rather by infinitely many statistical distributions. By considering the worst scenario among these potential distributions, the G-VaR predictor is precisely identified. Extensive experiments on both the NASDAQ Composite Index and S\\&P500 Index demonstrate the excellent performance of the G-VaR predictor, which is superior to most existing benchmark VaR predictors.
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中文摘要：
风险价值（VaR）是金融风险管理的一个主要工具，有几个成熟的基准预测因子。特别推荐将AR-GARCH滤波与倾斜残差和基于极值理论的方法相结合的混合方法。本研究引入了另一个VaR预测因子G-VaR，它采用了一种新的方法。受最新的次线性期望数学理论的启发，G-VaR建立在模型不确定性的概念上，在本案例中，这意味着财务回报的固有波动性不能由单一分布来表征，而是由无限多个统计分布来表征。通过考虑这些潜在分布中最坏的情况，可以精确地识别G-VaR预测值。在纳斯达克综合指数和S&P500指数上进行的大量实验表明，G-VaR预测器的性能优异，优于大多数现有的基准VaR预测器。
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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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