Value-at-Risk, Tail Value-at-Risk and upper tail transform of the sum of two counter-monotonic random variables

TL;DR

This paper extends the understanding of risk measures like VaR and TVaR for sums of two counter-monotonic random variables, providing new decomposition formulas that generalize previous results for arbitrary dependence structures.

Contribution

It introduces novel decomposition formulas for VaR, TVaR, and the stop-loss transform for sums of two counter-monotonic variables, generalizing prior work.

Findings

Decomposition formulas for VaR, TVaR, and stop-loss of counter-monotonic sums.

Extension of previous results to arbitrary counter-monotonic dependence.

Enhanced understanding of risk measure behavior under counter-monotonic dependence.

Abstract

The Value-at-Risk (VaR) of comonotonic sums can be decomposed into marginal VaR's at the same level. This additivity property allows to derive useful decompositions for other risk measures. In particular, the Tail Value-at-Risk (TVaR) and the upper tail transform of comonotonic sums can be written as the sum of their corresponding marginal risk measures. The other extreme dependence situation, involving the sum of two arbitrary counter-monotonic random variables, presents a certain number of challenges. One of them is that it is not straightforward to express the VaR of a counter-monotonic sum in terms of the VaR's of the marginal components of the sum. This paper generalizes the results derived in Chaoubi et al. (2020) by providing decomposition formulas for the VaR, TVaR and the stop-loss transform of the sum of two arbitrary counter-monotonic random variables.