- October 2010
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A multivariate generalized autoregressive conditional heteroscedasticity model with dynamic conditional correlations is proposed, in which the individual conditional volatilities follow exponential generalized
autoregressive conditional heteroscedasticity models and the
standardized innovations follow a mixture of Gaussian distributions.
Inference on the model parameters and prediction of future volatilities
are addressed by both maximum likelihood and Bayesian estimation
methods. Estimation of the Value at Risk of a given portfolio and
selection of optimal portfolios under the proposed specification are
addressed. The good performance of the proposed methodology is
illustrated via Monte Carlo experiments and the analysis of the daily
closing prices of the Dow Jones and NASDAQ indexes.