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Dynamic local models for segmentation and prediction of financial time series

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  • Mehdi Azzouzi
  • Ian Nabney

Abstract

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. Aspecial form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.

Suggested Citation

  • Mehdi Azzouzi & Ian Nabney, 2001. "Dynamic local models for segmentation and prediction of financial time series," The European Journal of Finance, Taylor & Francis Journals, vol. 7(4), pages 289-311.
    • Handle: RePEc:taf:eurjfi:v:7:y:2001:i:4:p:289-311
    DOI: 10.1080/13518470110071155
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    References listed on IDEAS

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    1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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