Multivariate GARCH models are in principle able to accommodate the features
of the dynamic conditional correlations processes, although with the drawback, when
the number of financial returns series considered increases, that the parameterizations
entail too many parameters.In general, the interaction between model parametrization
of the second conditional moment and the conditional density of asset returns
adopted in the estimation determines the fitting of such models to the observed dynamics
of the data. This paper aims to evaluate the interactions between conditional
second moment specifications and probability distributions adopted in the likelihood
computation, in forecasting volatilities and covolatilities. We measure the relative
performances of alternative conditional second moment and probability distributions
specifications by means of Monte Carlo simulations, using both statistical and financial
forecasting loss functions