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DC Field | Value | Language |
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dc.contributor.author | Roengchai Tansuchat | en_US |
dc.contributor.author | Woraphon Yamaka | en_US |
dc.date.accessioned | 2019-08-05T04:35:07Z | - |
dc.date.available | 2019-08-05T04:35:07Z | - |
dc.date.issued | 2019-01-01 | en_US |
dc.identifier.issn | 16113349 | en_US |
dc.identifier.issn | 03029743 | en_US |
dc.identifier.other | 2-s2.0-85064196946 | en_US |
dc.identifier.other | 10.1007/978-3-030-14815-7_18 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064196946&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/65541 | - |
dc.description.abstract | © Springer Nature Switzerland AG 2019. The aim of this paper is to propose smooth transition (ST) copula as new model to capture nonlinear or two regimes dependence structure between emerging and advanced stock markets, and compare the performance of ST copula with Markov-Switching (MS) copula and traditional copula. The data consists of two sets of stock markets, namely five emerging stock markets: China, India, Brazil, Indonesia, and Turkey, and two advanced stock markets: United Kingdom and United States of America. The results show that ST student-t copula for two-regime dependent structure outperforms MS copula, and one regime copula. Thus, ST copula is more appropriate model for the dependence structure between emerging and advanced stock markets. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | Nonlinear dependence structure in emerging and advanced stock markets | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
article.volume | 11471 LNAI | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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