Covariance Prediction in Large Portfolio Allocation

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. These matrices are used as inputs to obtain out-of-sample minimum variance portfolios based on all stocks belonging to the S&P500 index from 2000 to 2017. When considering the standard deviation of out-of-sample portfolio returns as the main performance metric, we find that DCC-based models estimated by composite likelihood and the RiskMetrics 2006 methodology deliver the best performance for portfolios rebalanced on both daily and monthly frequencies