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Measuring risk of crude oil at extreme quantiles

机译:在极端分位数下测量原油风险

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The purpose of this paper is to investigate the performance of VaR models at measuring risk for WTI oil one-month futures returns. Risk models, ranging from industry standards such as RiskMetrics and historical simulation to conditional extreme value model, are used to calculate commodity market risk at extreme quantiles: 0.95, 0.99, 0.995 and 0.999 for both long and short trading positions. Our results show that out of the tested fat tailed distributions, generalised Pareto distribution provides the best fit to both tails of oil returns although tails differ significantly, with the right tail having a higher tail index, indicative of more extreme events. The main conclusion is that, in the analysed period, only extreme value theory based models provide a reasonable degree of safety while widespread VaR models do not provide adequate risk coverage and their performance is especially weak for short position in oil.
机译:本文的目的是调查VaR模型在衡量WTI石油一个月期货收益风险时的性能。从行业标准(例如RiskMetrics和历史模拟到有条件的极值模型),风险模型用于计算极端分位数的商品市场风险:多头和空头头寸的0.95、0.99、0.995和0.999。我们的结果表明,在测试的胖尾分布中,广义帕累托分布最适合于回油的两个尾部,尽管尾部差异很大,而右尾部具有较高的尾部指数,表明发生了更多的极端事件。主要结论是,在分析期间,只有基于极值理论的模型才能提供合理的安全程度,而广泛使用的VaR模型不能提供足够的风险覆盖范围,并且它们的性能对于短仓石油尤为脆弱。

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