Monte Carlo Estimation of CoVaR

• 成果类型: Article
• 署名作者: Huang, Weihuan; Lin, Nifei; Hong, L. Jeff
• 署名单位: Nanjing University; Fudan University; Fudan University; Fudan University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.0211
• 发表日期: 2024
• 页码: 2337-2357
• 关键词: value-at-risk; Systemic risk; contagion

摘要

CoVaR is one of the most important measures of financial systemic risks. It is defined as the risk of a financial portfolio conditional on another financial portfolio being at risk. In this paper we first develop a Monte Carlo simulation-based batching estimator of CoVaR and study its consistency and asymptotic normality. We show that the best rate of convergence that the batching estimator can achieve is n(-1/3), where n is the sample size. We then develop an importance sampling-inspired estimator under the delta-gamma approximations to the portfolio losses and show that the best rate of convergence that the estimator can achieve is n(-1/2). Numerical experiments support our theoretical findings and show that both estimators work well.