Minkowski deviation measures

IF 1.3 Q2 STATISTICS & PROBABILITY
Marlon Moresco, Marcelo Brutti Righi, E. Horta
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引用次数: 1

Abstract

Abstract We propose to derive deviation measures through the Minkowski gauge of a given set of acceptable positions. We show that, given a suitable acceptance set, any positive homogeneous deviation measure can be accommodated in our framework. In doing so, we provide a new interpretation for such measures, namely, that they quantify how much one must shrink or deleverage a financial position for it to become acceptable. In particular, the Minkowski Deviation of a set which is convex, translation insensitive, and radially bounded at non-constants, is a generalized deviation measure in the sense of [R. T. Rockafellar, S. Uryasev and M. Zabarankin, Generalized deviations in risk analysis, Finance Stoch. 10 2006, 1, 51–74]. Furthermore, we explore the converse relations from properties of a Minkowski Deviation to its sub-level sets, introducing the notion of acceptance sets for deviations. Hence, we fill a gap existing in the literature, namely the lack of a well-defined concept of acceptance sets for deviation measures. Dual characterizations in terms of polar sets and support functionals are provided.
闵可夫斯基偏差测量
摘要我们建议通过给定一组可接受位置的Minkowski规范导出偏差度量。我们证明,在给定一个合适的接受集的情况下,任何正齐次偏差测度都可以被纳入我们的框架。在这样做的过程中,我们为这些措施提供了一种新的解释,即它们量化了一个人必须在多大程度上缩减或去杠杆化金融头寸才能被接受。特别是,一个集的Minkowski偏差是凸的、平移不敏感的、在非常数处径向有界的,是[R.T.Rockafellar、S.Uryasev和M.Zabarankin,风险分析中的广义偏差,Finance Stoch.10 2006,1,51–74]意义上的广义偏差测度。此外,我们还探讨了从Minkowski偏差的性质到其子级集的逆关系,引入了偏差的接受集的概念。因此,我们填补了文献中存在的一个空白,即缺乏一个明确定义的偏差度量接受集概念。给出了极点集和支持泛函的对偶刻画。
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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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