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arXiv 2404.13637q-fin.RM
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具有部分信息的失真风险度量的极端情况

Extremal cases of distortion risk measures with partial information

Qufu Normal University(曲阜师范大学);Central University of Finance and Economics(中央财经大学);McMaster University(麦克马斯特大学);Zhejiang University(浙江大学)

Mengshuo Zhao, Narayanaswamy Balakrishnan, Chuancun Yin, Hui Shao

AI总结:

研究在部分分布信息下,分布不确定性对关键风险度量的影响。通过概率不等式和修正施瓦茨不等式,为失真风险度量极端问题建立边界并明确分布特征,提供统一框架。

摘要:

本文研究了在仅了解基础分布的前两阶矩和形状信息(特别是对称性和/或单峰性)这种部分知识情况下,分布不确定性对关键风险度量的影响。首先利用概率不等式建立风险价值(VaR)的理论最佳和最坏情况边界,反映在矩和形状约束内可实现的最极端尾部风险。然后通过修正的施瓦茨不等式将这种最坏/最佳情况分析扩展到一类广泛的失真风险度量,得出在相同部分信息设置下它们相应的稳健边界。此外,明确了达到最佳和最坏情况的分布特征。该方法为失真风险度量的极端问题提供了统一框架。

英文摘要:

This paper investigates the impact of distributional uncertainty on key risk measures under the partial knowledge of underlying distributions characterized by their first two moments and shape information (specifically symmetry and/or unimodality). We first employ probability inequalities to establish the theoretical best- and worst-case bounds on Value-at-Risk, reflecting the most extreme tail risk achievable within the moment and shape constraints, and then we extend this worst-case/best-case analysis to a broad class of distortion risk measures by the modified Schwarz inequality, deriving their corresponding robust bounds under the same partial information setting concerning moments and distribution shapes of the underlying distributions. In addition, we give a clear characterization of the distributions that attain the best- and worst-case scenarios. The proposed approach provides a unified framework for extremal problems of distortion risk measures.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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