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dc.contributor.authorWang H
dc.contributor.authorPappadà R
dc.contributor.authorDurante F
dc.contributor.authorFoscolo E
dc.contributor.editorVantaggi B
dc.contributor.editorFerraro MB
dc.contributor.editorGiordani P
dc.contributor.editorGil MA
dc.contributor.editorGagolewski M
dc.contributor.editorGrzegorzewski P
dc.contributor.editorHryniewicz O
dc.date.accessioned2017-09-19T14:20:03Z
dc.date.available2017-09-19T14:20:03Z
dc.date.issued2017
dc.identifier.isbn978-3-319-42971-7
dc.identifier.issn2194-5357
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-42972-4_63
dc.identifier.urihttp://link.springer.com/chapter/10.1007/978-3-319-42972-4_63
dc.identifier.urihttp://hdl.handle.net/10863/3016
dc.description.abstractWe provide a two-stage portfolio selection procedure in order to increase the diversification benefits in a bear market. By exploiting tail dependence-based risky measures, a cluster analysis is carried out for discerning between assets with the same performance in risky scenarios. Then, the portfolio composition is determined by fixing a number of assets and by selecting only one item from each cluster. Empirical calculations on the EURO STOXX 50 prove that investing on selected assets in trouble periods may improve the performance of risk-averse investors.en_US
dc.language.isoenen_US
dc.publisherSpringer International Publishingen_US
dc.subjectSECS-S/01en_US
dc.subjectSECS-S/03en_US
dc.titleA portfolio diversification strategy via tail dependence clusteringen_US
dc.typeBook chapteren_US
dc.date.updated2016-12-16T07:48:25Z
dc.publication.titleSoft Methods for Data Science
dc.journal.titleAdvances in Intelligent Systems and Computing
dc.description.fulltextnoneen_US


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