On the Separability of Vector-Valued Risk Measures

TL;DR

This paper investigates the properties of vector-valued risk measures, revealing that convex, lower semicontinuous measures often ignore dependence structures, limiting their use in capital allocation and systemic risk assessment.

Contribution

It demonstrates that convex, lower semicontinuous vector-valued risk measures are insensitive to dependence structures, highlighting limitations for financial applications.

Findings

Convex, lower semicontinuous vector-valued risk measures ignore dependence structures.

Set-valued risk measures can account for dependence, unlike vector-valued ones.

Results extend to conditional risk measures.

Abstract

Risk measures for random vectors have been considered in multi-asset markets with transaction costs and financial networks in the literature. While the theory of set-valued risk measures provide an axiomatic framework for assigning to a random vector its set of all capital requirements or allocation vectors, the actual decision-making process requires an additional rule to select from this set. In this paper, we define vector-valued risk measures by an analogous list of axioms and show that, in the convex and lower semicontinuous case, such functionals always ignore the dependence structures of the input random vectors. We also show that set-valued risk measures do not have this issue as long as they do not reduce to a vector-valued functional. Finally, we demonstrate that our results also generalize to the conditional setting. These results imply that convex vector-valued risk measures are not suitable for defining capital allocation rules for a wide range of financial applications including systemic risk measures.