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dc.contributor.authorPayap Tarkhamthamen_US
dc.contributor.authorWoraphon Yamakaen_US
dc.contributor.authorParavee Maneejuken_US
dc.date.accessioned2021-01-27T04:16:54Z-
dc.date.available2021-01-27T04:16:54Z-
dc.date.issued2021-01-01en_US
dc.identifier.issn18609503en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-85096204749en_US
dc.identifier.other10.1007/978-3-030-48853-6_32en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85096204749&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/71869-
dc.description.abstract© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Google search volume index has been widely used as a proxy of investor attention. In this study, we use Google search volume index to forecast energy return volatility. In order to find the keywords, we start with glossary of crude oil terms provided by the U.S. Energy Information Administration (EIA) and then add keywords based on Google Search’s suggestions. Then, we arrive at a set of 75 Google keywords as a proxy of investor attention. As there are a large number of keywords to be considered, the conventional method may not be appropriate for the statistical inference. Thus, we propose using the LASSO to deal with this problem. Finally, we also compare the predictive power of LASSO with three types of stepwise method.en_US
dc.subjectComputer Scienceen_US
dc.titleForecasting Volatility of Oil Prices via Google Trend: LASSO Approachen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume898en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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