Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/53674
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dc.contributor.authorK. Autchariyapanitkulen_US
dc.contributor.authorS. Sriboonchittaen_US
dc.contributor.authorS. Chanaimen_US
dc.date.accessioned2018-09-04T09:55:12Z-
dc.date.available2018-09-04T09:55:12Z-
dc.date.issued2014-01-01en_US
dc.identifier.issn16860209en_US
dc.identifier.other2-s2.0-84907234273en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84907234273&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/53674-
dc.description.abstract© 2014 by the Mathematical Association of Thailand. All rights reserved. We used the multivariate t copula, which can capture the tail dependence to modeling the dependence structure of the risk in portfolio analysis. Multivariate t copula based on GARCH model was used to explain portfolio risk structure for high-dimensional asset allocation issue. With this method we used the Monte Carlo simulation and the results of multivariate t copula to estimate the expected shortfall of the portfolio. Finally, we obtained the optimal weighted for conditional Value-at-Risk (CVaR) model with the assumption of multivariate distribution to illustrate the potential model risk among portfolios returns.en_US
dc.subjectMathematicsen_US
dc.titlePortfolio optimization of stock returns in high-dimensions: A copula-based approachen_US
dc.typeJournalen_US
article.title.sourcetitleThai Journal of Mathematicsen_US
article.volume2014en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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