A Full-Factor Multivariate GARCH Model

Posted: 21 Oct 2004

See all articles by Ioannis D. Vrontos

Ioannis D. Vrontos

Athens University of Economics and Business

Petros Dellaportas

Athens University of Economics and Business

Dimitris N. Politis

University of California, San Diego (UCSD) - Department of Mathematics

Abstract

A new multivariate time series model with time varying conditional variances and covariances is presented and analysed. A complete analysis of the proposed model is presented consisting of parameter estimation, model selection and volatility prediction. Classical and Bayesian techniques are used for the estimation of the model parameters. It turns out that the construction of our proposed model allows easy maximum likelihood estimation and construction of well-mixing Markov chain Monte Carlo (MCMC) algorithms. Bayesian model selection is addressed using MCMC model composition. The problem of accounting for model uncertainty is considered using Bayesian model averaging. We provide implementation details and illustrations using daily rates of return on eight stocks of the US market.

Keywords: Autoregressive conditional heteroscedasticity, Bayesian model averaging, Markov chain Monte Carlo model composition, Maximum likelihood estimation

JEL Classification: C11, C51, C52

Suggested Citation

Vrontos, Ioannis D. and Dellaportas, Petros and Politis, Dimitris, A Full-Factor Multivariate GARCH Model. Available at SSRN: https://ssrn.com/abstract=606901

Ioannis D. Vrontos (Contact Author)

Athens University of Economics and Business ( email )

76 Patission Street
Athens, 104 34
Greece

Petros Dellaportas

Athens University of Economics and Business ( email )

76 Patission Street
Athens, 104 34
Greece

Dimitris Politis

University of California, San Diego (UCSD) - Department of Mathematics ( email )

9500 Gilman Drive
La Jolla, CA 92093-0112
United States
858-534-5861 (Phone)
858-534-5273 (Fax)

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