A more accurate forecast of covariance matrices can greatly improve how well investment portfolios perform. Different methods were tested to see which one works best. The results show that using a multivariate GARCH forecast instead of a sample covariance matrix forecast can cut errors in half and make portfolios track better. The findings also suggest that an exponentially weighted covariance matrix forecast is almost as good as the multivariate GARCH forecast.