Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.number | 8 | - |
dc.citation.startPage | 375 | - |
dc.citation.title | AXIOMS | - |
dc.citation.volume | 11 | - |
dc.contributor.author | Kim, Jong-Min | - |
dc.contributor.author | Han, Hope H. | - |
dc.contributor.author | Kim, Sangjin | - |
dc.date.accessioned | 2023-12-21T13:45:30Z | - |
dc.date.available | 2023-12-21T13:45:30Z | - |
dc.date.created | 2022-09-15 | - |
dc.date.issued | 2022-08 | - |
dc.description.abstract | This paper introduces methodologies in forecasting oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. We also apply Bayesian variable selection and nonlinear principal component analysis (NLPCA) for data dimension reduction. With a reduced number of important covariates, we also forecast oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. To apply real data to the proposed methods, we select monthly log returns of 2 oil prices and 74 large-cap, major S&P 500 stock prices across the period of February 2001-October 2019. We conclude that vine copula regression with NLPCA is superior overall to other proposed methods in terms of the measures of prediction errors. | - |
dc.identifier.bibliographicCitation | AXIOMS, v.11, no.8, pp.375 | - |
dc.identifier.doi | 10.3390/axioms11080375 | - |
dc.identifier.issn | 2075-1680 | - |
dc.identifier.scopusid | 2-s2.0-85137349001 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/59592 | - |
dc.identifier.wosid | 000846325900001 | - |
dc.language | 영어 | - |
dc.publisher | MDPI | - |
dc.title | Forecasting Crude Oil Prices with Major S&P 500 Stock Prices: Deep Learning, Gaussian Process, and Vine Copula | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Applied | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | oil prices | - |
dc.subject.keywordAuthor | S&P 500 | - |
dc.subject.keywordAuthor | multivariate time series | - |
dc.subject.keywordAuthor | Gaussian process model | - |
dc.subject.keywordAuthor | vine copula | - |
dc.subject.keywordAuthor | Bayesian variable selection | - |
dc.subject.keywordAuthor | functional principal component analysis | - |
dc.subject.keywordAuthor | nonlinear principal component analysis | - |
dc.subject.keywordPlus | MOVEMENTS | - |
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