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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14307
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| Title: | Predicting Daily Probability Distributions Of S&P500 Returns |
| Authors: | Weigend, Andreas S.
Shi, Shanming |
| 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 |
| Issue Date: | Aug-1998 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-98-23 |
| 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. |
| URI: | http://hdl.handle.net/2451/14307 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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