Law invariant risk measures and information divergences

TL;DR

This paper establishes a correspondence between law invariant risk measures and divergences, exploring their properties, dualities, and implications for dynamic risk assessment, with a focus on classical divergences like relative entropy.

Contribution

It introduces a novel link between risk measures and divergences, analyzes their properties, and characterizes relative entropy as uniquely satisfying the chain rule.

Findings

Divergences include classical measures like relative entropy and f-divergences.

Properties of divergences relate to time consistency in dynamic risk measures.

Relative entropy is essentially the only divergence satisfying the chain rule.

Abstract

A one-to-one correspondence is drawn between law invariant risk measures and divergences, which we define as functionals of pairs of probability measures on arbitrary standard Borel spaces satisfying a few natural properties. Divergences include many classical information divergence measures, such as relative entropy and $f$-divergences. Several properties of divergence and their duality with law invariant risk measures are developed, most notably relating their chain rules or additivity properties with certain notions of time consistency for dynamic law invariant risk measures known as acceptance and rejection consistency. These properties are linked also to a peculiar property of the acceptance sets on the level of distributions, analogous to results of Weber on weak acceptance and rejection consistency. Finally, the examples of shortfall risk measures and optimized certainty equivalents are discussed in some detail, and it is shown that the relative entropy is essentially the only divergence satisfying the chain rule.