Predicting daily probability distributions of S&P500 returns

This paper presents ‘hidden Markov experts’, a framework for predicting conditional probability distributions of future values of a time series. On daily S&P500 data, the out-of- sample performance is compared to several baselines including GARCH and ‘gated experts’. The evaluation of the full density shows improvement over all competitors. Since the performance for point-predictions is comparable to the other methods, the main advantage of hidden Markov experts is their use for conditional density forecasting.