M. Denuit, Jan Dhaene, M. Goovaerts, R. Kaas, R. Laeven
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引用次数: 73
摘要
风险度量在精算文献中已经研究了几十年,它们在保费计算原则的幌子下出现。最近,在金融数学文献中,风险度量和风险度量应满足的属性受到了相当大的关注。在数学上,风险度量是从一类随机变量到实线的映射。从经济上讲,风险度量应该捕捉决策者的偏好。本文对Denuit, Dhaene & Van Wouwe(1999)的研究进行了补充,并考虑了几种不确定决策理论:经典期望效用范式、Yaari的二元方法、最大期望效用理论、Choquet期望效用理论和Quiggin的等级依赖效用理论。在精算等效效用定价原则的基础上,产生了广泛类别的风险度量,其中最经典的风险度量似乎是特殊情况。这种方法表明,最近在金融数学文献中研究的大多数风险度量都忽略了效用概念(即对应于线性效用),限制了它们的适用性。讨论了文献中提出的一些替代方案。
Risk measurement with equivalent utility principles
SUMMARY Risk measures have been studied for several decades in the actuarial literature, where they appeared under the guise of premium calculation principles. Risk measures and properties that risk measures should satisfy have recently received considerable attention in the financial mathematics literature. Mathematically, a risk measure is a mapping from a class of random variables to the real line. Economically, a risk measure should capture the preferences of the decision-maker. This paper complements the study initiated in Denuit, Dhaene & Van Wouwe (1999) and considers several theories for decision under uncertainty: the classical expected utility paradigm, Yaari's dual approach, maximin expected utility theory, Choquet expected utility theory and Quiggin's rank-dependent utility theory. Building on the actuarial equivalent utility pricing principle, broad classes of risk measures are generated, of which most classical risk measures appear to be particular cases. This approach shows that most risk measures studied recently in the financial mathematics literature disregard the utility concept (i.e., correspond to linear utilities), restricting their applicability. Some alternatives proposed in the literature are discussed.
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.