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Multiday expected shortfall under generalized t distributions: evidence from global stock market

机译:广泛性的T分布下的多日期预期缺口:来自全球股市的证据

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We apply seven alternative t-distributions to estimate the market risk measures Value at Risk (VaR) and its extension Expected Shortfall (ES). Of these seven, the twin t-distribution (TT) of Baker and Jackson (in Twin t distribution, University of Salford Manchester. ) and generalized asymmetric distribution (GAT) of Baker (in A new asymmetric generalization of the t-distribution, University of Salford Manchester. ) are applied for the first time to estimate market risk. We analytically estimate VaR and ES over 1-day horizon and extend this to multi-day horizon using Monte Carlo simulation. We find that taken together TT and GAT distributions provide the best back-testing results across individual confidence levels and horizons for majority of scenarios. Moreover, we find that with the lengthening of time horizon, TT and GAT models performs well, such that at the 10-day horizon, GAT provides the best back-testing results for all of the five indices and the TT model provides the second best results, irrespective period of study and confidence level.
机译:我们申请七个替代T分布来估计风险(VAR)的市场风险措施价值及其延伸预期的差分差额。在这七个,贝克和杰克逊的双胞胎分配(TT)(在萨尔福德大学曼彻斯特大学。)和贝克的广义不对称分布(GAT)(在T分布的新的不对称概括中,大学萨尔福德曼彻斯特。)首次申请估计市场风险。我们分析了var和es超过1天的地平线,并使用蒙特卡罗模拟将此扩展到多日地平线。我们发现,在一起的TT和GAT分布在各个情景中提供了各个置信水平和视野的最佳反击测试结果。此外,我们发现,随着时间地平线的延长,TT和GAT模型表现良好,使得在10天的地平线上,GAT为所有五个指数提供了最佳的反向测试结果,而TT模型则提供第二个索引结果,无论研究和置信水平如何。

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