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초록
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.
키워드
Joint Prediction Region; Multivariate-GARCH; Value at Risk(VaR); Joint Prediction Region; Multivariate-GARCH; Value at Risk(VaR)
- 제목
- Multivariate GARCH and Its Application to Bivariate Time Series
- 저자
- 최문선; 박진아; 황선영
- 발행일
- 2007-12
- 저널명
- 한국데이터정보과학회지
- 권
- 18
- 호
- 4
- 페이지
- 915 ~ 925