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dc.contributor.authorPınar, M. Ç.en_US
dc.date.accessioned2018-04-12T13:44:41Z
dc.date.available2018-04-12T13:44:41Z
dc.date.issued2007en_US
dc.identifier.issn0171-6468
dc.identifier.urihttp://hdl.handle.net/11693/38110
dc.description.abstractWe develop and test multistage portfolio selection models maximizing expected end-of-horizon wealth while minimizing one-sided deviation from a target wealth level. The trade-off between two objectives is controlled by means of a non-negative parameter as in Markowitz Mean-Variance portfolio theory. We use a piecewise-linear penalty function, leading to linear programming models and ensuring optimality of subsequent stage decisions. We adopt a simulated market model to randomly generate scenarios approximating the market stochasticity. We report results of rolling horizon simulation with two variants of the proposed models depending on the inclusion of transaction costs, and under different simulated stock market conditions. We compare our results with the usual stochastic programming models maximizing expected end-of-horizon portfolio value. The results indicate that the robust investment policies are indeed quite stable in the face of market risk while ensuring expected wealth levels quite similar to the competing expected value maximizing stochastic programming model at the expense of solving larger linear programs.en_US
dc.language.isoEnglishen_US
dc.source.titleOR Spectrumen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s00291-005-0023-2en_US
dc.subjectDiscrete scenario treeen_US
dc.subjectDownside risken_US
dc.subjectFinanceen_US
dc.subjectMulti-period portfolio selectionen_US
dc.subjectRisken_US
dc.subjectStochastic programmingen_US
dc.titleRobust scenario optimization based on downside-risk measure for multi-period portfolio selectionen_US
dc.typeReviewen_US
dc.departmentDepartment of Industrial Engineering
dc.citation.spage295en_US
dc.citation.epage309en_US
dc.citation.volumeNumber29en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.1007/s00291-005-0023-2en_US
dc.publisherSpringeren_US
dc.identifier.eissn1436-6304


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