Multivariate GARCH Model and its Application to Bivariate Model
Nelson Nainggolan1, Hanny Andrea Huibert Komalig2, Tohap Manurung3
1Nelson Nainggolan, Department of Mathematics, Faculty of Mathematics and Natural Science, Sam Ratulangi University, Indonesia.
2Hanny Andrea Huibert Komalig, Department of Mathematics, Faculty of Mathematics and Natural Science, Sam Ratulangi University, Indonesia.
3Tohap Manurung, Department of Mathematics, Faculty of Mathematics and Natural Science, Sam Ratulangi University, Indonesia.
Manuscript received on 02 August 2019 | Revised Manuscript received on 25 August 2019 | Manuscript Published on 05 September 2019 | PP: 111-114 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10250782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1025.0782S719
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Multivariate, GARCH, Maximum Likelihood.
Scope of the Article: Cryptography and Applied Mathematics