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dc.contributor.authorKittawit Autchariyapanitkulen_US
dc.contributor.authorSutthiporn Piamsuwannakiten_US
dc.contributor.authorSomsak Chanaimen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T02:58:11Z-
dc.date.available2018-09-05T02:58:11Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-84952690582en_US
dc.identifier.other10.1007/978-3-319-27284-9_20en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952690582&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55585-
dc.description.abstract© Springer International Publishing Switzerland 2016. We applied the vine copulas, which can measure the dependence structure of uncertainty in portfolio investments. C-vine and D-vine copulas based on capital asset pricing models were used to exhibit portfolio risk structure in the content of asset allocation. With this approach, we employed the Monte Carlo simulation and the empirical results of C-vine and D-vine copulas to determine the expected shortfall of an optimally weighted portfolio. Furthermore, we used the condition Value-at-Risk (CVaR) model with the assumption of C-vine and D-vine joint distribution to gain the maximum returns in portfolios.en_US
dc.subjectComputer Scienceen_US
dc.titleOptimizing Stock Returns Portfolio Using the Dependence Structure Between Capital Asset Pricing Models: A Vine Copula-Based Approachen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume622en_US
article.stream.affiliationsMaejo Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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