A reverse ES (CVaR) optimization formula

TL;DR

This paper introduces a reverse optimization formula for Expected Shortfall (ES) that reveals symmetries between the mean excess function and ES, extending to convex risk measures and demonstrating practical applications.

Contribution

It establishes a novel reverse ES optimization formula, connecting mean excess functions and ES curves, and generalizes the result to optimized certainty equivalents.

Findings

Reveals symmetries between mean excess and ES functions

Provides a reverse optimization formula related to Fenchel-Legendre transforms

Demonstrates practical applications with insurance data

Abstract

The celebrated Expected Shortfall (ES) optimization formula implies that ES at a fixed probability level is the minimum of a linear real function plus a scaled mean excess function. We establish a reverse ES optimization formula, which says that a mean excess function at any fixed threshold is the maximum of an ES curve minus a linear function. Despite being a simple result, this formula reveals elegant symmetries between the mean excess function and the ES curve, as well as their optimizers. The reverse ES optimization formula is closely related to the Fenchel-Legendre transforms, and our formulas are generalized from ES to optimized certainty equivalents, a popular class of convex risk measures. We analyze worst-case values of the mean excess function under two popular settings of model uncertainty to illustrate the usefulness of the reverse ES optimization formula, and this is further demonstrated with an application using insurance datasets.