# Worst-Case Expected Shortfall with Univariate and Bivariate Marginals

## TL;DR

This paper introduces a new worst-case expected shortfall bound leveraging univariate marginals and bivariate expert information, balancing conservatism and confidence via a divergence-based neighborhood, with efficient computation for tree-structured marginals.

## Contribution

It develops a novel bound on expected shortfall using bivariate marginals and divergence neighborhoods, enabling flexible risk assessment with expert input.

## Key findings

- Bound reduces to known bounds at extreme divergence values
- Efficient convex optimization approach for tree-structured marginals
- Framework accommodates inconsistent marginals and other expert parameters

## Abstract

Worst-case bounds on the expected shortfall risk given only limited information on the distribution of the random variables has been studied extensively in the literature. In this paper, we develop a new worst-case bound on the expected shortfall when the univariate marginals are known exactly and additional expert information is available in terms of bivariate marginals. Such expert information allows for one to choose from among the many possible parametric families of bivariate copulas. By considering a neighborhood of distance $\rho$ around the bivariate marginals with the Kullback-Leibler divergence measure, we model the trade-off between conservatism in the worst-case risk measure and confidence in the expert information. Our bound is developed when the only information available on the bivariate marginals forms a tree structure in which case it is efficiently computable using convex optimization. For consistent marginals, as $\rho$ approaches $\infty$, the bound reduces to the comonotonic upper bound and as $\rho$ approaches $0$, the bound reduces to the worst-case bound with bivariates known exactly. We also discuss extensions to inconsistent marginals and instances where the expert information which might be captured using other parameters such as correlations.