Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/99089
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dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorMi, Hen_US
dc.creatorXu, Zen_US
dc.date.accessioned2023-06-14T01:00:14Z-
dc.date.available2023-06-14T01:00:14Z-
dc.identifier.issn0167-6687en_US
dc.identifier.urihttp://hdl.handle.net/10397/99089-
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.rights© 2023 Elsevier B.V. All rights reserved.en_US
dc.rights© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Mi, H., & Xu, Z. Q. (2023). Optimal portfolio selection with VaR and portfolio insurance constraints under rank-dependent expected utility theory. Insurance: Mathematics and Economics, 110, 82-105 is available at https://dx.doi.org/10.1016/j.insmatheco.2023.02.004.en_US
dc.subjectPortfolio optimizationen_US
dc.subjectRank-dependent expected utilityen_US
dc.subjectQuantile formulationen_US
dc.subjectRelaxation methoden_US
dc.subjectVaR constrainten_US
dc.titleOptimal portfolio selection with VaR and portfolio insurance constraints under rank-dependent expected utility theoryen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage82en_US
dc.identifier.epage105en_US
dc.identifier.volume110en_US
dc.identifier.doi10.1016/j.insmatheco.2023.02.004en_US
dcterms.abstractThis paper investigates two optimal portfolio selection problems for a rank-dependent utility investor who needs to manage his risk exposure: one with a single Value-at-Risk (VaR) constraint and the other with joint VaR and portfolio insurance constraints. The two models generalize existing models under expected utility theory and behavioral theory. The martingale method, quantile formulation, and relaxation method are used to obtain explicit optimal solutions. We have specifically identified an equivalent condition under which the VaR constraint is effective. A numerical analysis is carried out to demonstrate theoretical results, and additional financial insights are presented. We find that, in bad market states, the risk of the optimal investment outcome is reduced when compared to existing models without or with one constraint.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInsurance : mathematics and economics, May 2023, v. 110, p. 82-105en_US
dcterms.isPartOfInsurance : mathematics and economicsen_US
dcterms.issued2023-05-
dc.identifier.scopus2-s2.0-85150452901-
dc.identifier.eissn1873-5959en_US
dc.description.validate202306 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera2099; a3419b-
dc.identifier.SubFormID46604; 50094-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNSFC; Nanjing Normal University; PolyU-SDU Joint Research Center on Financial Mathematics; CAS AMSS-PolyU Joint Laboratory of Applied Mathematics; Hong Kong Polytechnic University Research Centre for Quantitative Financeen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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