  We suggest two classes of multivariate GARCH--models which are both easy to
estimate and perform well in forecasting the covariance matrix of more than one
hundred stocks. We apply methods from random matrix theory (RMT) to determine
the number of principal components or the number of factors in the multivariate
volatility models. In this way only statistically relevant information is used
for the estimation of model parameters.
