Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55587
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dc.contributor.authorNantiworn Thianpaenen_US
dc.contributor.authorSomsak Chanaimen_US
dc.contributor.authorJirakom Sirisrisakulchaien_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T02:58:12Z-
dc.date.available2018-09-05T02:58:12Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-84952700783en_US
dc.identifier.other10.1007/978-3-319-27284-9_23en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952700783&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55587-
dc.description.abstract© Springer International Publishing Switzerland 2016. This paper aims to find the optimal Global Healthcare Portfolios at different levels of risks and returns to obtain the efficient frontier. The risks are measured by expected shortfall. The dependency of selected stocks in portfolios cannot be ignored. The high-dimension copula-models are used to capture the dependency parameters of the selected stocks. Five largest market capitalization stocks in the global healthcare sector are selected for this analysis. According to the Akaike Information Criterion (AIC), the empirical results show that t-copula is better fitted between the t- and the Gaussian copulas. Based on the t-copula, the result of this study which is the efficient frontier of the global healthcare portfolios is finally shown in Table 4 for related decision makers.en_US
dc.subjectComputer Scienceen_US
dc.titleEfficient frontier of global healthcare portfolios using high dimensions of copula modelsen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume622en_US
article.stream.affiliationsRajabhat Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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