scholarly journals An improved estimator of the distortion risk measure for heavy-tailed claims

2014 ◽  
Vol 44 (20) ◽  
pp. 1-1
Author(s):  
Abdelaziz Rassoul
2016 ◽  
Vol 68 ◽  
pp. 101-109 ◽  
Author(s):  
Jaume Belles-Sampera ◽  
Montserrat Guillen ◽  
Miguel Santolino

2013 ◽  
Vol 83 (12) ◽  
pp. 2703-2710 ◽  
Author(s):  
Wenhua Lv ◽  
Xiaoqing Pan ◽  
Taizhong Hu

2017 ◽  
Vol 76 ◽  
pp. 28-47 ◽  
Author(s):  
Qing Liu ◽  
Liang Peng ◽  
Xing Wang
Keyword(s):  

Author(s):  
Peter W. Glynn ◽  
Yijie Peng ◽  
Michael C. Fu ◽  
Jian-Qiang Hu

Distortion risk measure, defined by an integral of a distorted tail probability, has been widely used in behavioral economics and risk management as an alternative to expected utility. The sensitivity of the distortion risk measure is a functional of certain distribution sensitivities. We propose a new sensitivity estimator for the distortion risk measure that uses generalized likelihood ratio estimators for distribution sensitivities as input and establish a central limit theorem for the new estimator. The proposed estimator can handle discontinuous sample paths and distortion functions.


This chapter introduces some alternative risk measures to Vale-At-Risk (VaR) calculations: Extreme Value Theory (EVT), Expected Shortfall (ES) and distortion risk measure. It also discusses their more coherent characteristics useful for shoring up the weaknesses of VaR.


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