Multivariate GARCH models are in principle able to accommodate the features of the dynamic conditional covariances; nonetheless 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. Alternative MGARCH specifications and probability distributions are compared on the basis of forecasting performances by means of Monte Carlo simulations, using both statistical and financial forecasting loss functions.
Model and distribution uncertainty in Multivariate GARCH estimation: a Monte Carlo analysis
ROSSI, EDUARDO;
2010-01-01
Abstract
Multivariate GARCH models are in principle able to accommodate the features of the dynamic conditional covariances; nonetheless 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. Alternative MGARCH specifications and probability distributions are compared on the basis of forecasting performances by means of Monte Carlo simulations, using both statistical and financial forecasting loss functions.File in questo prodotto:
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