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Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review

Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review L. JEFF HONG, City University of Hong Kong ZHAOLIN HU, Tongji University GUANGWU LIU, City University of Hong Kong 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. Categories and Subject Descriptors: I.6.3 [Simulation and Modeling]: Applications General Terms: Theory, Algorithms, Management Additional Key Words and Phrases: Financial risk management, value-at-risk, conditional value-at-risk ACM Reference Format: L. Jeff Hong, Zhaolin Hu, and Guangwu Liu. 2014. Monte Carlo methods for value-at-risk and conditional value-at-risk: A review. ACM Trans. Model. Comput. Simul. 24, 4, Article 22 (November 2014), 37 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Modeling and Computer Simulation (TOMACS) Association for Computing Machinery

Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
1049-3301
DOI
10.1145/2661631
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Abstract

Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review L. JEFF HONG, City University of Hong Kong ZHAOLIN HU, Tongji University GUANGWU LIU, City University of Hong Kong 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. Categories and Subject Descriptors: I.6.3 [Simulation and Modeling]: Applications General Terms: Theory, Algorithms, Management Additional Key Words and Phrases: Financial risk management, value-at-risk, conditional value-at-risk ACM Reference Format: L. Jeff Hong, Zhaolin Hu, and Guangwu Liu. 2014. Monte Carlo methods for value-at-risk and conditional value-at-risk: A review. ACM Trans. Model. Comput. Simul. 24, 4, Article 22 (November 2014), 37

Journal

ACM Transactions on Modeling and Computer Simulation (TOMACS) – Association for Computing Machinery

Published: Nov 13, 2014

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