Please use this identifier to cite or link to this item: https://doi.org/10.1287/mnsc.1070.0769
DC FieldValue
dc.titleIncorporating asymmetric distributional information in robust value-at-risk optimization
dc.contributor.authorNatarajan, K.
dc.contributor.authorPachamanova, D.
dc.contributor.authorSim, M.
dc.date.accessioned2013-10-09T06:18:04Z
dc.date.available2013-10-09T06:18:04Z
dc.date.issued2008
dc.identifier.citationNatarajan, K., Pachamanova, D., Sim, M. (2008). Incorporating asymmetric distributional information in robust value-at-risk optimization. Management Science 54 (3) : 573-585. ScholarBank@NUS Repository. https://doi.org/10.1287/mnsc.1070.0769
dc.identifier.issn00251909
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/44184
dc.description.abstractValue-at-Risk (VaR) is one of the most widely accepted risk measures in the financial and insurance industries, yet efficient optimization of VaR remains a very difficult problem. We propose a computationally tractable approximation method for minimizing the VaR of a portfolio based on robust optimization techniques. The method results in the optimization of a modified VaR measure, Asymmetry-Robust VaR (ARVaR), that takes into consideration asymmetries in the distributions of returns and is coherent, which makes it desirable from a financial theory perspective. We show that ARVaR approximates the Conditional VaR of the portfolio as well. Numerical experiments with simulated and real market data indicate that the proposed approach results in lower realized portfolio VaR, better efficient frontier, and lower maximum realized portfolio loss than alternative approaches for quantile-based portfolio risk minimization. © 2008 INFORMS.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1287/mnsc.1070.0769
dc.sourceScopus
dc.subjectCoherent risk measures
dc.subjectRobust optimization
dc.subjectValue-at-risk
dc.typeArticle
dc.contributor.departmentMATHEMATICS
dc.contributor.departmentDECISION SCIENCES
dc.description.doi10.1287/mnsc.1070.0769
dc.description.sourcetitleManagement Science
dc.description.volume54
dc.description.issue3
dc.description.page573-585
dc.description.codenMSCIA
dc.identifier.isiut000254050600012
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