Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/65541
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dc.contributor.authorRoengchai Tansuchaten_US
dc.contributor.authorWoraphon Yamakaen_US
dc.date.accessioned2019-08-05T04:35:07Z-
dc.date.available2019-08-05T04:35:07Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn16113349en_US
dc.identifier.issn03029743en_US
dc.identifier.other2-s2.0-85064196946en_US
dc.identifier.other10.1007/978-3-030-14815-7_18en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064196946&origin=inwarden_US
dc.identifier.urihttp://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.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleNonlinear dependence structure in emerging and advanced stock marketsen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume11471 LNAIen_US
article.stream.affiliationsChiang Mai Universityen_US
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