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摘要翻译：
准确的风险预测是成功的风险管理技术的关键。本文以欧洲12家证券交易所最大的股指期货为研究对象，在单期和多期条件下，分别给出了基于无条件分布和条件分布的VaR测度。这些以极值理论为基础的度量在统计上是稳健的，明确地考虑了厚尾密度。用GARCH滤波后的返回值对无条件极值过程进行调整，得到条件尾估计。条件建模导致iid返回，允许使用简单有效的多周期极值标度律。本文考察了这些不同的有条件交易模型和无条件交易模型的性质。本文发现，假设正态的无条件单周期和多周期估计中固有的偏差扩展到条件设置。
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英文标题：
《Varying the VaR for Unconditional and Conditional Environments》
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作者：
John Cotter
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最新提交年份：
2011
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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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英文摘要：
Accurate forecasting of risk is the key to successful risk management techniques. Using the largest stock index futures from twelve European bourses, this paper presents VaR measures based on their unconditional and conditional distributions for single and multi-period settings. These measures underpinned by extreme value theory are statistically robust explicitly allowing for fat-tailed densities. Conditional tail estimates are obtained by adjusting the unconditional extreme value procedure with GARCH filtered returns. The conditional modelling results in iid returns allowing for the use of a simple and efficient multi-period extreme value scaling law. The paper examines the properties of these distinct conditional and unconditional trading models. The paper finds that the biases inherent in unconditional single and multi-period estimates assuming normality extend to the conditional setting.
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PDF链接：
https://arxiv.org/pdf/1103.5649
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