Risk, utility and sensitivity to large losses

Martin Herdegen, Nazem Khan, Cosimo Munari
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引用次数: 1

Abstract

Risk and utility functionals are fundamental building blocks in economics and finance. In this paper we investigate under which conditions a risk or utility functional is sensitive to the accumulation of losses in the sense that any sufficiently large multiple of a position that exposes an agent to future losses has positive risk or negative utility. We call this property sensitivity to large losses and provide necessary and sufficient conditions thereof that are easy to check for a very large class of risk and utility functionals. In particular, our results do not rely on convexity and can therefore also be applied to most examples discussed in the recent literature, including (non-convex) star-shaped risk measures or S-shaped utility functions encountered in prospect theory. As expected, Value at Risk generally fails to be sensitive to large losses. More surprisingly, this is also true of Expected Shortfall. By contrast, expected utility functionals as well as (optimized) certainty equivalents are proved to be sensitive to large losses for many standard choices of concave and nonconcave utility functions, including $S$-shaped utility functions. We also show that Value at Risk and Expected Shortfall become sensitive to large losses if they are either properly adjusted or if the property is suitably localized.
风险、效用和对巨额损失的敏感性
风险和效用函数是经济学和金融学的基本构件。在本文中,我们研究了在哪些条件下,风险或效用函数对损失的累积敏感,即任何足够大的头寸倍数都会使代理人面临未来的损失,并具有正风险或负效用。我们将这一特性称为对巨额损失的敏感性,并提供了必要条件和充分条件,这些条件很容易对一大类风险和效用函数进行检验。特别是,我们的结果并不依赖于凸性,因此也可以应用于近期文献中讨论的大多数例子,包括前景理论中遇到的(非凸性)星形风险度量或 S 形效用函数。不出所料,风险价值通常对巨额损失不敏感。更令人惊讶的是,预期亏损也是如此。相比之下,对于许多标准选择的凹形和非凹形效用函数,包括 S$ 形效用函数,预期效用函数以及(优化的)确定性等价物都被证明对巨额损失很敏感。我们还证明,如果对风险价值和预期亏损进行适当调整,或者对该属性进行适当局部化,它们就会对巨额损失变得敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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