Scholion.ai

Quantile-Based Risk Sharing

Paul Embrechts, Haiyan Liu, Ruodu Wang

Operations Research · 2018 · Volume 66 · Issue 4

DOI: 10.1287/opre.2017.1716

Abstract

We address the problem of risk sharing among agents using a two-parameter class of quantile-based risk measures, the so-called range-value-at-risk (RVaR), as their preferences. The family of RVaR includes the value-at-risk (VaR) and the expected shortfall (ES), the two popular and competing regulatory risk measures, as special cases. We first establish an inequality for RVaR-based risk aggregation, showing that RVaR satisfies a special form of subadditivity. Then, the Pareto-optimal risk sharing problem is solved through explicit construction. To study risk sharing in a competitive market, an Arrow–Debreu equilibrium is established for some simple yet natural settings. Furthermore, we investigate the problem of model uncertainty in risk sharing and show that, in general, a robust optimal allocation exists if and only if none of the underlying risk measures is a VaR. Practical implications of our main results for risk management and policy makers are discussed, and several novel advantages of ES over VaR from the perspective of a regulator are thereby revealed. The e-companion is available at https://doi.org/10.1287/opre.2017.1716 .

Keywords

value-at-risk, expected shortfall, risk sharing, regulatory capital, robustness, Arrow–Debreu equilibrium

Enable JavaScript for citation tools and additional paper interactions.