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Hidden Markov Experts

In: Quantitative Analysis In Financial Markets Collected Papers of the New York University Mathematical Finance Seminar(Volume II)

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  • ANDREAS S. WEIGEND

    (ShockMarket Corporation, 151 Lytton Avenue, Palo Alto, CA 94301, USA)

  • SHANMING SHI

    (J. P. Morgan & Co. Inc., 60 Wall Street, New York, NY 10260, USA)

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 non-linear 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(1,1) 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.

Suggested Citation

  • Andreas S. Weigend & Shanming Shi, 2001. "Hidden Markov Experts," World Scientific Book Chapters, in: Marco Avellaneda (ed.), Quantitative Analysis In Financial Markets Collected Papers of the New York University Mathematical Finance Seminar(Volume II), chapter 2, pages 35-70, World Scientific Publishing Co. Pte. Ltd..
  • Handle: RePEc:wsi:wschap:9789812810663_0002
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