Monte Carlo Estimation of CoVaR

Weihuan Huang (), Nifei Lin () and L. Jeff Hong ()
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Weihuan Huang: School of Management & Engineering, Nanjing University, Nanjing 210093, China
Nifei Lin: School of Management, Fudan University, Shanghai 200433, China
L. Jeff Hong: School of Management and School of Data Science, Fudan University, Shanghai 200433, China

Operations Research, 2024, vol. 72, issue 6, 2337-2357

Abstract: 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.

Keywords: Financial Engineering; systemic risk; CoVaR; Monte Carlo simulation; batching; delta-gamma approximation; importance sampling; statistical analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:72:y:2024:i:6:p:2337-2357

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