Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/71885
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dc.contributor.authorBing Yangen_US
dc.contributor.authorPayap Tarkhamthamen_US
dc.contributor.authorPongsutti Phuensanen_US
dc.contributor.authorKongliang Zhuen_US
dc.date.accessioned2021-01-27T04:17:01Z-
dc.date.available2021-01-27T04:17:01Z-
dc.date.issued2021-01-01en_US
dc.identifier.issn18609503en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-85089916974en_US
dc.identifier.other10.1007/978-3-030-49728-6_25en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089916974&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/71885-
dc.description.abstract© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. In this study, we aim to investigate the high dimension portfolio optimization by using Markov Switching Copula-based GJR-GARCH model. The proposed model is flexible and can capture the dependence structure that changes over time. This model is applied to 8 times series, including DJIA, FTSE, COMEX Gold, US Dollar Index, Crude Oil, and US Bonds (one-month, 2-year, and 5-year. In order to construct a portfolio, first we use GJR-GARCH to capture the volatility of each asset. Then, the Markov Switching copula is used to measure the dependence across assets. Finally, the results from MS-Copula is used to construct portfolios and Value at Risk and Expected Shortfall are used for optimal portfolio selection.en_US
dc.subjectComputer Scienceen_US
dc.titlePortfolios optimization under regime switching model: Evidences in the american bonds and other financial assetsen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume897en_US
article.stream.affiliationsKhon Kaen Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
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