Open AccessMultivariate GARCH Model and Its Application to Bivariate Model
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1025.0782s719
Subject(s) - multivariate statistics , autoregressive conditional heteroskedasticity , univariate , bivariate analysis , heteroscedasticity , econometrics , mathematics , statistics , covariance , multivariate analysis , conditional variance , volatility (finance)
Multivariate GARCH model is a development of the univariate GARCH model. The multivariate GARCH model can be viewed as a conditional heteroskedasticity model in a multivariate time series. This paper discusses the parameterization of covariance matrices such as Vech model representation, BEEK model and Constant Correlation model. For parameter estimation the maximum likelihood method is used. Furthermore, multivariate GARCH model application is applied for bivariate model