VaR bounds in models with partial dependence information on subgroups
VaR bounds in models with partial dependence information on subgroups
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DOI：
10.1515/demo-2017-0004
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发表时间：
2017-01
影响因子：
0.7
通讯作者：
L. Rüschendorf;Julian Witting
中科院分区：
文献类型：
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作者：
L. Rüschendorf;Julian Witting
当除了边际信息外，还存在部分相关信息时，我们得到了风险投资组合模型风险的改进估计.我们考虑被分成k个子组的模型，并考虑子组内或子组间的各种相关信息.因此，我们得到了改进的联合投资组合的风险价值界的情况下，只有信息的边缘。我们的论文增加了最近的各种方法，以获得可靠和可用的风险范围。通过包括除了关于边缘的信息之外的部分依赖信息来估计模型风险。特别是，我们扩展了Biggeli，Puccetti和Rüschendorf（2015）以及Puccetti，Rüschendorf，Small和Vanduelf（2017）提出的方法，该方法分别基于正相关。关于独立性的信息可供一些分组使用。
Abstract We derive improved estimates for the model risk of risk portfolios when additional to the marginals some partial dependence information is available.We consider models which are split into k subgroups and consider various classes of dependence information either within the subgroups or between the subgroups. As consequence we obtain improved VaR bounds for the joint portfolio compared to the case with only information on the marginals. Our paper adds to various recent approaches to obtain reliable and usable risk bounds resp. estimates of the model risk by including partial dependence information additional to the information on the marginals. In particular we extend an approach suggested in Bignozzi, Puccetti and Rüschendorf (2015) and in Puccetti, Rüschendorf, Small and Vanduffel (2017), which is based on positive dependence resp. on independence information available for some subgroups.