Predicting Daily Probability Distributions Of S&P500 Returns
|Authors:||Weigend, Andreas S.|
|Keywords:||Forecasting;Density Prediction;Conditional Distribution;Mixture Models;Time Series Analysis;Hidden Markov Models;Gated Experts;Hidden Markov Experts;Model Comparison;Density Evaluation;Computational Finance;Risk Management|
|Publisher:||Stern School of Business, New York University|
|Abstract:||Most approaches in forecasting merely try to predict the next value of the time series. 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 the dynamics between the states is modeled using a hidden Markov approach. The full density predictions are obtained by a weighted superposition 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 improve over simpler models, evaluating the prediction 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, the specialization of experts, the removal of outliers, and the persistence of volatility.|
|Appears in Collections:||IOMS: Information Systems Working Papers|
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