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Why is CVaR superior to VaR? (c2009)

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dc.contributor.author Dalleh, Nivine
dc.date.accessioned 2011-10-25T07:38:28Z
dc.date.available 2011-10-25T07:38:28Z
dc.date.copyright 2009 en_US
dc.date.issued 2011-10-25
dc.date.submitted 2009-07
dc.identifier.uri http://hdl.handle.net/10725/881
dc.description Includes bibliographical references (l. 80-82). en_US
dc.description.abstract Until recently, value-at-risk (VaR) has been a widely used risk measure in portfolio optimization. The large number of recent bank failures shows that VaR failed to account for the expected losses which resulted from the outburst of a rare event such as the global financial crisis, thereby questioning its reliability and credibility as a measure of risk. Alternatively, previous work concurs that conditional value-at-risk (CVaR) is a coherent tail risk measure, and has established the superiority of CVaR over traditional measures of risk (variance and VaR) from a theoretical standpoint. This study aims at investigating the reasons that render CVaR superior to other traditional risk measures from an empirical perspective. We develop a theoretical model that solves the mean-risk portfolio optimization problem within a unified framework for all three different measures of risk (variance, VaR, and CVaR). We test our model empirically using financial data on return indices over a period covering the financial crisis. Our results support the theoretical predictions regarding the superiority of CVaR. We find that the mean-CVAR framework can be applied to multi-model returns, unlike mean-variance (where variance is a dispersion measure) and mean-VaR (where VaR is anon-coherent risk measure) which are only valid when returns are normal. The mean-CVaR framework respects diversification, and we find that CVaR is the most conservative measure of risk. en_US
dc.language.iso en en_US
dc.subject Financial futures en_US
dc.subject Risk management en_US
dc.subject Portfolio management en_US
dc.subject Value at risk en_US
dc.title Why is CVaR superior to VaR? (c2009) en_US
dc.type Thesis en_US
dc.title.subtitle A unified framework for mean-risk portfolio optimization en_US
dc.term.submitted Summer I en_US
dc.author.school Business en_US
dc.author.idnumber 200201402 en_US
dc.author.commembers Dr. Bernard Ben Sita en_US
dc.author.commembers Dr. Raymond Ghajar en_US
dc.author.woa OA en_US
dc.author.department Master of Bus. Administration en_US
dc.description.physdesc 1 bound copy: iv, 91 leaves; ill.; 30 cm. available at RNL. en_US
dc.author.division Economics en_US
dc.author.advisor Dr. Rima Ariss en_US
dc.keywords Portfolio optimization en_US
dc.keywords Risk en_US
dc.keywords Variance en_US
dc.keywords Value-at-risk en_US
dc.keywords Conditional value-at-risk en_US
dc.keywords Linear programming en_US
dc.identifier.doi https://doi.org/10.26756/th.2009.47


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