Counter-monotonic Risk Sharing with Heterogeneous Distortion Risk Measures

Fuente: arXiv
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Hauptverfasser: Ghossoub, Mario, Ren, Qinghua, Wang, Ruodu
Format: Preprint
Veröffentlicht: 2024
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author Ghossoub, Mario
Ren, Qinghua
Wang, Ruodu
author_facet Ghossoub, Mario
Ren, Qinghua
Wang, Ruodu
contents We study risk sharing among agents with preferences modeled by heterogeneous distortion risk measures, who are not necessarily risk averse. Pareto optimality for agents using risk measures is often studied through the lens of inf-convolutions, because allocations that attain the inf-convolution are Pareto optimal, and the converse holds true under translation invariance. Our main focus is on groups of agents who exhibit varying levels of risk seeking. Under mild assumptions, we derive explicit solutions for the unconstrained inf-convolution and the counter-monotonic inf-convolution, which can be represented by a generalization of distortion risk measures.
format Preprint
id arxiv_https___arxiv_org_abs_2412_00655
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Counter-monotonic Risk Sharing with Heterogeneous Distortion Risk Measures
Ghossoub, Mario
Ren, Qinghua
Wang, Ruodu
Risk Management
91G99
We study risk sharing among agents with preferences modeled by heterogeneous distortion risk measures, who are not necessarily risk averse. Pareto optimality for agents using risk measures is often studied through the lens of inf-convolutions, because allocations that attain the inf-convolution are Pareto optimal, and the converse holds true under translation invariance. Our main focus is on groups of agents who exhibit varying levels of risk seeking. Under mild assumptions, we derive explicit solutions for the unconstrained inf-convolution and the counter-monotonic inf-convolution, which can be represented by a generalization of distortion risk measures.
title Counter-monotonic Risk Sharing with Heterogeneous Distortion Risk Measures
topic Risk Management
91G99
url https://arxiv.org/abs/2412.00655