Multivariate GARCH and Its Application to Bivariate Time Series
- Authors
- 최문선; 박진아; 황선영
- Issue Date
- Dec-2007
- Publisher
- 한국데이터정보과학회
- Keywords
- Joint Prediction Region; Multivariate-GARCH; Value at Risk(VaR); Joint Prediction Region; Multivariate-GARCH; Value at Risk(VaR)
- Citation
- 한국데이터정보과학회지, v.18, no.4, pp 915 - 925
- Pages
- 11
- Journal Title
- 한국데이터정보과학회지
- Volume
- 18
- Number
- 4
- Start Page
- 915
- End Page
- 925
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/14792
- ISSN
- 1598-9402
- 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.
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