🤖 AI Summary
This paper addresses robust risk aggregation and allocation under dependence uncertainty, focusing on the Range-Value-at-Risk (RVaR) and related variability measures within the Fréchet framework. Methodologically, it establishes the first sharp inequalities for RVaR, introduces extended convolution bounds, and integrates comonotonic analysis, nonconvex optimization, and dependence modeling. The theoretical contributions are threefold: (1) tight bounds for RVaR, inter-RVaR differences, and quantile differences; (2) characterization of optimal risk-sharing structures—comonotonic for large losses and countermonotonic for small losses/large gains; and (3) an explicit closed-form optimal allocation achieving the minimal average quantile-based risk, with a proof that no optimal solution exists when risks are unbounded above. These results strengthen the theoretical foundation of Fréchet problems in quantitative risk management.
📝 Abstract
In this paper, we provide extended convolution bounds for the Fréchet problem and discuss related implications in quantitative risk management. First, we establish a new form of inequality for the Range-Value-at-Risk (RVaR). Based on this inequality, we obtain bounds for robust risk aggregation with dependence uncertainty for (i) RVaR, (ii) inter-RVaR difference and (iii) inter-quantile difference, and provide sharpness conditions. These bounds are called extended convolution bounds, which not only complement the results in the literature (convolution bounds in Blanchet et al. (2025)) but also offer results for some variability measures. Next, applying the above inequality, we study the risk sharing for the averaged quantiles (corresponding to risk sharing for distortion risk measures with special inverse S-shaped distortion functions), which is a non-convex optimization problem. We obtain the expression of the minimal value of the risk sharing and the explicit expression for the corresponding optimal allocation, which is comonotonic risk sharing for large losses and counter-comonotonic risk sharing for small losses or large gains. Finally, we explore the dependence structure for the optimal allocations, showing that the optimal allocation does not exist if the risk is not bounded from above.