Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/54360
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dc.contributor.authorKittawit Autchariyapanitkulen_US
dc.contributor.authorSomsak Chanaimen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-04T10:12:26Z-
dc.date.available2018-09-04T10:12:26Z-
dc.date.issued2015-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-84919360816en_US
dc.identifier.other10.1007/978-3-319-13449-9_15en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84919360816&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/54360-
dc.description.abstract© Springer International Publishing Switzerland 2015. We used a quantile regression under asymmetric Laplace distribution for predicting stock returns. Specifically, we apply this method to the classical capital asset pricing model (CAPM) to estimate the beta coefficient which measure risk in the portfolios management analysis at given levels of quantile. Quantile regression estimation is equivalent to the parametric case where the error term is asymmetrically Laplace distributed. Finally, we use the method to measures the volatility of a portfolio relative to the market.en_US
dc.subjectComputer Scienceen_US
dc.titleQuantile regression under asymmetric laplace distribution in capital asset pricing modelen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume583en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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