关于能力和风险度量的随机优势

IF 1.3 Q2 STATISTICS & PROBABILITY Statistics & Risk Modeling Pub Date : 2014-01-01 DOI:10.1515/strm-2014-1167
Miryana Grigorova
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引用次数: 11

摘要

在我们之前的工作中,我们将经典的凸随机优势关系相对于概率增加的概念扩展到更一般的归一化单调(但不一定是加性)集合函数,也称为容量的情况。在本文中,我们通过研究广义随机优势关系下满足共单调可加性和相合性的货币风险测度集(定义在有界实值可测函数空间上)来继续这项工作。在适当的潜在容量空间假设下,我们用关于扭曲容量的Choquet积分来描述这类风险测度,其扭曲函数是凹的。草冈类型的特征也被建立。给出了尾值处于风险状态的能力的推广情况作为一个例子。本文还证明了关于扭曲概率的Choquet积分的一些众所周知的结果在更一般的扭曲容量情况下不一定成立。
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Stochastic dominance with respect to a capacity and risk measures
Abstract In our previous work, we have extended the classical notion of increasing convex stochastic dominance relation with respect to a probability to the more general case of a normalized monotone (but not necessarily additive) set function, also called a capacity. In the present paper, we pursue that work by studying the set of monetary risk measures (defined on the space of bounded real-valued measurable functions) satisfying the properties of comonotonic additivity and consistency with respect to the generalized stochastic dominance relation. Under suitable assumptions on the underlying capacity space, we characterize that class of risk measures in terms of Choquet integrals with respect to a distorted capacity whose distortion function is concave. Kusuoka-type characterizations are also established. A generalization to the case of a capacity of the Tail Value at Risk is provided as an example. It is also shown that some well-known results about Choquet integrals with respect to a distorted probability do not necessarily hold true in the more general case of a distorted capacity.
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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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