Risk, Utility and Sensitivity to Large Losses

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.

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Publication Details

Journal
Mathematical Finance
Published
2026-10-14
DOI
https://doi.org/10.1111/mafi.70065
Primary Topic
Risk and Portfolio Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Risk, Utility and Sensitivity to Large Losses

Martin Herdegen, Cosimo Munari, Nazem Khan
Mathematical Finance
Risk and Portfolio Optimization
article

Risk, Utility and Sensitivity to Large Losses

Martin Herdegen, Cosimo Munari, Nazem Khan
article en

Abstract

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.

Mathematical Finance
University of Stuttgart (DE), University of Verona (IT), University of Warwick (GB)
Openalex Percentile: Top 9%
Risk and Portfolio Optimization
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