Abstract

Abstract: Most approaches in forecasting merely try to predict the next value of the time se-ries. In contrast, this paper presents a framework to predict the full probability distribution. It is expressed as a mixture model: the dynamics of the individual states is modeled with so-called "experts " (potentially nonlinear neural networks), and tbe dynamics between the states is modeled using a hidden Markov approach. The full density predictions are obtained by a weighted superposi-tion of the individual densities of each expert. This model class is called "hidden Markov experts". Results are presented for daily S&P500 data. While the predictive accuracy of the mean does not irrlprove over simpler models, evaluating the prediclion of the full density shows a clear out-of-sample improvement both over a simple GARCH(1,l) model (which assumes Gaussian distributed returns) and over a "gated experts " model (which expresses the weighting for each state non-recursively as a function of external inputs). Several interpretations are given: the blending of supervised and unsupervised learning, the discovery of hidden states, the combination of forecasts,