Predicting daily probability distributions of S&P500 returns

Andreas S. Weigend · Shanming Shi

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Financial Risk and Volatility Modeling Forecasting Techniques and Applications Stock Market Forecasting Methods

Abstract

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. Copyright © 2000 John Wiley & Sons, Ltd.

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References

1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle 2010International Economic Review 1994Business-Cycle Phases and Their Transitional Dynamics 1992The likelihood ratio test under nonstandard conditions: Testing the markov switching model of gnp 1993Calculating Interval Forecasts 1994Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth

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