Monte carlo methods for value-at-risk and conditional value-at-risk : A review

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number5
Journal / PublicationACM Transactions on Modeling and Computer Simulation
Volume24
Issue number4
Early online date13 Aug 2014
Publication statusPublished - Aug 2014

Abstract

Value-at-risk (VaR) and conditional value-at-risk (CVaR) are two widely used risk measures of large losses and are employed in the financial industry for risk management purposes. In practice, loss distributions typically do not have closed-form expressions, but they can often be simulated (i.e., random observations of the loss distribution may be obtained by running a computer program). Therefore, Monte Carlo methods that design simulation experiments and utilize simulated observations are often employed in estimation, sensitivity analysis, and optimization of VaRs and CVaRs. In this article, we review some of the recent developments in these methods, provide a unified framework to understand them, and discuss their applications in financial risk management.

Research Area(s)

  • Conditional value-at-risk, Financial risk management, Value-at-risk