Building an Early Warning System for Crude Oil Price Using Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 송원호 | - |
dc.date.available | 2019-08-20T04:59:24Z | - |
dc.date.issued | 2010-12 | - |
dc.identifier.issn | 2508-1640 | - |
dc.identifier.issn | 2508-1667 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/34670 | - |
dc.description.abstract | In this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN) model is used to construct the early warning system. Most early warning systems are built based on the signaling approach. In this paper, we show that the neural network models are more flexible and have greater potential as EWS than the signaling approach. Third, we allow the multi-level crisis index. Previous models allowed only a zero/one crisis index whereas our model permits as many levels as possible. With this new model, we try to answer whether the oil price collapse following the historical peak in 2008 was predictable. We compare the results from the NN model with those from the ordered probit (OP) model, and show that the oil price crisis and the following crash were predictable by the NN model, but not by the OP model. | - |
dc.format.extent | 41 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 대외경제정책연구원 | - |
dc.title | Building an Early Warning System for Crude Oil Price Using Neural Network | - |
dc.title.alternative | 신경망모형을 사용한 국제유가의 조기경보시스템 구축 | - |
dc.type | Article | - |
dc.identifier.doi | 10.11644/KIEP.JEAI.2010.14.2.219 | - |
dc.identifier.bibliographicCitation | East Asian Economic Review, v.14, no.2, pp 79 - 119 | - |
dc.identifier.kciid | ART001504260 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 119 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 79 | - |
dc.citation.title | East Asian Economic Review | - |
dc.citation.volume | 14 | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | 국제유가 | - |
dc.subject.keywordAuthor | 조기경보시스템 | - |
dc.subject.keywordAuthor | 신경망 | - |
dc.subject.keywordAuthor | 순위 프로빗모형(ordered probit model) | - |
dc.subject.keywordAuthor | Crude Oil Price | - |
dc.subject.keywordAuthor | Early Warning System | - |
dc.subject.keywordAuthor | Neural Network | - |
dc.subject.keywordAuthor | Ordered Probit Model | - |
dc.description.journalRegisteredClass | kci | - |
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