Monte Carlo and importance sampling estimators of CoVaR

被引：0

作者：

Jiang, Guangxin ^{[1
]}

Hao, Jianshu ^{[1
]}

Sun, Tong ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Management, 92 Xidazhi St, Harbin 150001, Heilongjiang, Peoples R China

来源：

OPERATIONS RESEARCH LETTERS | 2025年 / 60卷

基金：

中国国家自然科学基金;

关键词：

Monte Carlo simulation; Importance sampling; CoVaR; Consistency; Asymptotic normality; SYSTEMIC RISK;

D O I：

10.1016/j.orl.2025.107250

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

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.

引用

页数：7

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