Multivariate GARCH and Its Application to Bivariate Time Series
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최문선 | - |
dc.contributor.author | 박진아 | - |
dc.contributor.author | 황선영 | - |
dc.date.available | 2021-02-22T15:03:48Z | - |
dc.date.issued | 2007-12 | - |
dc.identifier.issn | 1598-9402 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/14792 | - |
dc.description.abstract | Multivariate GARCH has been useful to model dynamic relationships between volatilities arising from each component series of multivariate time series. Methodologies including EWMA(Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model) models are comparatively reviewed for bivariate time series. In addition, these models are applied to evaluate VaR(Value at Risk) and to construct joint prediction region. To illustrate, bivariate stock prices data consisting of Samsung Electronics and LG Electronics are analysed. | - |
dc.format.extent | 11 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 한국데이터정보과학회 | - |
dc.title | Multivariate GARCH and Its Application to Bivariate Time Series | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.bibliographicCitation | 한국데이터정보과학회지, v.18, no.4, pp 915 - 925 | - |
dc.citation.title | 한국데이터정보과학회지 | - |
dc.citation.volume | 18 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 915 | - |
dc.citation.endPage | 925 | - |
dc.identifier.kciid | ART001203038 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Joint Prediction Region | - |
dc.subject.keywordAuthor | Multivariate-GARCH | - |
dc.subject.keywordAuthor | Value at Risk(VaR) | - |
dc.subject.keywordAuthor | Joint Prediction Region | - |
dc.subject.keywordAuthor | Multivariate-GARCH | - |
dc.subject.keywordAuthor | Value at Risk(VaR) | - |
dc.identifier.url | http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07244130 | - |
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