The Gaussian Mixture Dynamic Conditional Correlation Model: Parameter Estimation, Value at Risk Calculation, and Portfolio Selection Articles uri icon

publication date

  • October 2010

start page

  • 559

end page

  • 571

issue

  • 4

volume

  • 28

international standard serial number (ISSN)

  • 0735-0015

electronic international standard serial number (EISSN)

  • 1537-2707

abstract

  • 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.