资本风险分配的公允估计

IF 1.3 Q2 STATISTICS & PROBABILITY Statistics & Risk Modeling Pub Date : 2019-02-26 DOI:10.1515/strm-2019-0011
T. Bielecki, Igor Cialenco, Marcin Pitera, Thorsten Schmidt
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引用次数: 1

摘要

摘要本文提出了一种新的风险资本配置估计方法。该方法论植根于风险度量理论。我们在一类通用但易于处理的法律不变连贯风险度量中工作,特别关注预期短缺。我们引入了公平资本分配的概念,并在风险投资组合的组成部分共同正态分布的情况下,提供了公平资本配置的明确公式。本文的主要关注点是在不完全已知投资组合组成定律的情况下,近似公平投资组合分配的问题。我们定义并研究了公平分配估计和渐近公平分配估计的概念。我们研究的很大一部分致力于估计预期短缺的公平风险分配问题。我们在正态和非参数设置下研究了这个问题。我们推导了几个估计量,并证明了它们的公平性和/或渐近公平性。最后,但并非最不重要的是,我们提出了两种反向测试方法,旨在评估分配估计过程的性能。论文最后对该主题进行了大量的数值研究,并将其应用于市场数据。
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Fair estimation of capital risk allocation
Abstract In this paper, we develop a novel methodology for estimation of risk capital allocation. The methodology is rooted in the theory of risk measures. We work within a general, but tractable class of law-invariant coherent risk measures, with a particular focus on expected shortfall. We introduce the concept of fair capital allocations and provide explicit formulae for fair capital allocations in case when the constituents of the risky portfolio are jointly normally distributed. The main focus of the paper is on the problem of approximating fair portfolio allocations in the case of not fully known law of the portfolio constituents. We define and study the concepts of fair allocation estimators and asymptotically fair allocation estimators. A substantial part of our study is devoted to the problem of estimating fair risk allocations for expected shortfall. We study this problem under normality as well as in a nonparametric setup. We derive several estimators, and prove their fairness and/or asymptotic fairness. Last, but not least, we propose two backtesting methodologies that are oriented at assessing the performance of the allocation estimation procedure. The paper closes with a substantial numerical study of the subject and an application to market data.
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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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