Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58548
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dc.contributor.authorPanida Fanpaengen_US
dc.contributor.authorWoraphon Yamakaen_US
dc.contributor.authorRoengchai Tansuchaten_US
dc.date.accessioned2018-09-05T04:26:09Z-
dc.date.available2018-09-05T04:26:09Z-
dc.date.issued2018-01-01en_US
dc.identifier.issn16113349en_US
dc.identifier.issn03029743en_US
dc.identifier.other2-s2.0-85043998564en_US
dc.identifier.other10.1007/978-3-319-75429-1_30en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85043998564&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/58548-
dc.description.abstract© 2018, Springer International Publishing AG, part of Springer Nature. This paper investigates dynamic interaction among international volatility indexes, consisting of VIX, VSTOXX, VDAX, VFTSE, VNVIXN, VHSI and VKOSPI. This paper also extends the multivariate normal distribution and multivariate student-t distribution based dynamic conditional correlation (DCC) model to a multivariate skew distribution. We then apply this extended model to estimate the dynamic volatility and correlation in international volatility indexes. The empirical results of model comparison reveal the multivariate skewed student-t distribution based CGARCH-DCC model to perform the best in our real data analysis. This indicates that the time-varying conditional correlation coefficients as well as volatility are skewed and fat tailed or leptokurtic in characteristic.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleInvestigating Dynamic Correlation in the International Implied Volatility Indexesen_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.volume10758 LNAIen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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