A Full-Factor Multivariate GARCH Model
Posted: 21 Oct 2004
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
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