Model-free bounds on Value-at-Risk using extreme value information and statistical distances

TL;DR

This paper develops new model-free bounds on Value-at-Risk by incorporating extreme value information and statistical distances, improving risk estimates with partial dependence data.

Contribution

It introduces a unified framework for deriving VaR bounds using extreme value info, known copula subsets, and proximity measures, enhancing existing bounds.

Findings

Additional dependence information significantly tightens VaR bounds.

The approach applies to various dependence structures, including partial copula knowledge.

Numerical examples demonstrate improved bounds over marginal-only estimates.

Abstract

We derive bounds on the distribution function, therefore also on the Value-at-Risk, of $\varphi(\mathbf X)$ where $\varphi$ is an aggregation function and $\mathbf X = (X_1,\dots,X_d)$ is a random vector with known marginal distributions and partially known dependence structure. More specifically, we analyze three types of available information on the dependence structure: First, we consider the case where extreme value information, such as the distributions of partial minima and maxima of $\mathbf X$, is available. In order to include this information in the computation of Value-at-Risk bounds, we utilize a reduction principle that relates this problem to an optimization problem over a standard Fr\'echet class, which can then be solved by means of the rearrangement algorithm or using analytical results. Second, we assume that the copula of $\mathbf X$ is known on a subset of its domain, and finally we consider the case where the copula of $\mathbf X$ lies in the vicinity of a reference copula as measured by a statistical distance. In order to derive Value-at-Risk bounds in the latter situations, we first improve the Fr\'echet--Hoeffding bounds on copulas so as to include this additional information on the dependence structure. Then, we translate the improved Fr\'echet--Hoeffding bounds to bounds on the Value-at-Risk using the so-called improved standard bounds. In numerical examples we illustrate that the additional information typically leads to a significant improvement of the bounds compared to the marginals-only case.