A Simulation-Based Method for Estimating Systemic Risk Measures.

This paper focuses on simulation-based approaches for estimating systemic risk measures. In particular, we provide the asymptotic forms of the relative errors for widely used systemic risk measures includ-ing conditional value-at-risk (CoVaR), coexpected shortfall (CoES) and marginal expected shortfall (MES). Based on asymptotic expansions, a general framework is provided for the simulation of systemic risk measures. The numerical results show that the proposed simulation framework works well, and it is more user-friendly, easier to expand and less time-consuming than simulation approaches using the re -sampling method and importance sampling. (c) 2023 Elsevier B.V. All rights reserved.