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Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/54413
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dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorOlga Koshelevaen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-04T10:13:07Z-
dc.date.available2018-09-04T10:13:07Z-
dc.date.issued2015-01-01en_US
dc.identifier.issn03029743en_US
dc.identifier.other2-s2.0-84951004094en_US
dc.identifier.other10.1007/978-3-319-25135-6-14en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84951004094&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/54413-
dc.description.abstract© Springer International Publishing Switzerland 2015. Economic and financial processes are complex and highly nonlinear. However, somewhat surprisingly, linear models like ARMAX-GARCH often describe these processes reasonably well. In this paper, we provide a possible explanation for the empirical success of these models.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleWhy ARMAX-GARCH linear models successfully describe complex nonlinear phenomena: A possible explanationen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)en_US
article.volume9376en_US
article.stream.affiliationsNew Mexico State University Las Crucesen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
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