一般风险域凸风险泛函的最优风险分配

IF 1.3 Q2 STATISTICS & PROBABILITY
S. Kiesel, L. Rüschendorf
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引用次数: 1

摘要

摘要本文给出了定义在实Banach空间乘积上的凸风险泛函作为风险域的经典最优风险分配问题。这种通用性尤其包括Lp风险的经典案例,但也允许描述风险分配问题中依赖的影响。我们描述了最优分配,并完成了文献中已知的存在唯一性结果。我们将详细讨论预期风险函数的应用。这种情况可以通过应用于与风险泛函相关的Orlicz心的Banach空间方法来处理。我们给出了必要的连续性和可微性的详细讨论。基于Orlicz心的排序结果,将最优分配结果推广到不同的Orlicz心作为风险泛函域,并建立了经典Borch定理的一般形式。在一些数值算例中,确定了期望风险情况下的最优重分布,并对数值计算的精度进行了检验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal risk allocation for convex risk functionals in general risk domains
Abstract In this paper, we formulate the classical optimal risk allocation problem for convex risk functionals defined on products of real Banach spaces as risk domains. This generality includes in particular the classical case of Lp risks but also allows to describe the influence of dependence in the risk allocation problem. We characterize optimal allocations and complete known existence and uniqueness results from the literature. We discuss in detail an application to expected risk functionals. This case can be dealt with by the Banach space approach applied to Orlicz hearts associated to the risk functionals. We give a detailed discussion of the necessary continuity and differentiability properties. Based on ordering results for Orlicz hearts we obtain extensions of the optimal allocation results to different Orlicz hearts as domain of risk functionals and establish a general form of the classical Borch theorem. In some numerical examples, optimal redistributions are determined for the expected risk case and the precision of the numerical calculation is checked.
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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