222 / 2024-04-23 15:11:31

Monte Carlo and Importance Sampling Estimators of CoVaR

Monte Carlo Simulation,Importance Sampling,CoVaR,Asymptotic Normality

Assessing systemic risk in network systems, such as finance and supply chains, is crucial due to the potential propagation of risks from key nodes, impacting the entire system. In this paper, we introduce a Monte Carlo (MC) simulation approach to estimate CoVaR, which is one of the commonly used systemic risk measures and captures the tail dependency of losses between network systems and nodes. Additionally, given that CoVaR may involve rare events, we propose an importance sampling (IS) approach to enhance the efficiency of the estimation. We also establish consistency and asymptotic normality for both MC and IS estimators. Finally, we illustrate the effectiveness of our approach through numerical experiments.