Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks

被引:2
|
作者
Mao, Tiantian [1 ]
Stupfler, Gilles [2 ]
Yang, Fan [3 ]
机构
[1] Univ Sci & Technol China, Sch Management, Dept Stat & Finance, Hefei 230026, Peoples R China
[2] Univ Angers, CNRS, LAREMA, SFR MATHST, F-49000 Angers, France
[3] Univ Waterloo, Dept Stat & Actuanal Sci, Waterloo, ON N2L 3G1, Canada
来源
基金
加拿大自然科学与工程研究理事会; 中国国家自然科学基金;
关键词
Generalized shortfall risk measure; Asymptotic expansions; Heavy tails; Estimation; Extreme value statistics; 2ND-ORDER REGULAR VARIATION; PROSPECT-THEORY; QUANTILES; EXPANSIONS; EXPECTILES; ECONOMICS;
D O I
10.1016/j.insmatheco.2023.05.001
中图分类号
F [经济];
学科分类号
02 ;
摘要
We study a general risk measure called the generalized shortfall risk measure, which was first introduced in Mao and Cai (2018). It is proposed under the rank-dependent expected utility framework, or equivalently induced from the cumulative prospect theory. This risk measure can be flexibly designed to capture the decision maker's behavior toward risks and wealth when measuring risk. In this paper, we derive the first-and second-order asymptotic expansions for the generalized shortfall risk measure. Our asymptotic results can be viewed as unifying theory for, among others, distortion risk measures and utility-based shortfall risk measures. They also provide a blueprint for the estimation of these measures at extreme levels, and we illustrate this principle by constructing and studying a quantile-based estimator in a special case. The accuracy of the asymptotic expansions and of the estimator is assessed on several numerical examples.& COPY; 2023 Elsevier B.V. All rights reserved.
引用
收藏
页码:173 / 192
页数:20
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