{"title":"Monte Carlo and importance sampling estimators of CoVaR","authors":"Guangxin Jiang, Jianshu Hao, Tong Sun","doi":"10.1016/j.orl.2025.107250","DOIUrl":null,"url":null,"abstract":"<div><div>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. Given that CoVaR may involve rare events, we propose an importance sampling (IS) approach to enhance the efficiency of 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.</div></div>","PeriodicalId":54682,"journal":{"name":"Operations Research Letters","volume":"60 ","pages":"Article 107250"},"PeriodicalIF":0.8000,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research Letters","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167637725000112","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
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. Given that CoVaR may involve rare events, we propose an importance sampling (IS) approach to enhance the efficiency of 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.
期刊介绍：
Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.
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