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  • 标题:New Class of Distortion Risk Measures and Their Tail Asymptotics with Emphasis on VaR
  • 本地全文:下载
  • 作者:Chuancun Yin ; Dan Zhu
  • 期刊名称:Journal of Financial Risk Management
  • 印刷版ISSN:2167-9533
  • 电子版ISSN:2167-9541
  • 出版年度:2018
  • 卷号:07
  • 期号:01
  • 页码:12-38
  • DOI:10.4236/jfrm.2018.71002
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要:Distortion risk measures are extensively used in finance and insurance applications because of their appealing properties. We present three methods to construct new class of distortion functions and measures. The approach involves the composting methods, the mixing methods and the approach that based on the theory of copula. We also investigate the tail subadditivity for VaR and other distortion risk measures. In particular, we demonstrate that VaR is tail subadditive for the case where the support of risk is bounded. Various examples are also presented to illustrate the results.
  • 关键词:Coherent Risk Measure;Copula;Distortion Risk Measure;Extreme Value Theory;GlueVaR;Tail Subadditivity;Tail Distortion Risk Measure
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