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摘要翻译：
本文应用AR(1)-GARCH(1,1）过程详细描述了标准普尔500、FT100、DAX、恒生和日经225期货合约收益率分布的条件分布。然后，它使用这些合同的条件分布来估计谱风险度量，这些谱风险度量是反映用户风险厌恶函数的相干风险度量。本文将这些方法与更常见的VaR和预期缺口(ES)风险度量方法进行了比较，还比较了这些风险度量方法的精确度，并讨论了它们在设置包含时变市场条件的变化幅度方面的相对有用性。通过多种回试验证了模型的拟合优度。
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英文标题：
《Spectral Risk Measures with an Application to Futures Clearinghouse
Variation Margin Requirements》
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作者：
John Cotter and Kevin Dowd
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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：
This paper applies an AR(1)-GARCH (1, 1) process to detail the conditional distributions of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses the conditional distribution for these contracts to estimate spectral risk measures, which are coherent risk measures that reflect a user's risk-aversion function. It compares these to more familiar VaR and Expected Shortfall (ES) measures of risk, and also compares the precision and discusses the relative usefulness of each of these risk measures in setting variation margins that incorporate time-varying market conditions. The goodness of fit of the model is confirmed by a variety of backtests.
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PDF链接：
https://arxiv.org/pdf/1103.5408
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