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Gradient Boosting GARCH and Neural Networks for Time Series Prediction

  • José M. Matías
  • Manuel Febrero
  • Wenceslao González-Manteiga
  • Juan C. Reboredo
Part of the Studies in Computational Intelligence book series (SCI, volume 126)

Summary

This work develops and evaluates new algorithms based on neural networks and boosting techniques, designed to model and predict heteroskedastic time series. The main novel elements of these new algorithms are as follows: a) in regard to neural networks, the simultaneous estimation of conditional mean and volatility through the likelihood maximization; b) in regard to boosting, its simultaneous application to trend and volatility components of the likelihood, and the use of likelihood-based models (e.g. GARCH) as the base hypothesis rather than gradient fitting techniques using least squares. The behavior of the proposed algorithms is evaluated over simulated data, resulting in frequent and significant improvements in relation to the ARMA-GARCH models.

Keywords

gradient boosting GARCH ARMA neural networks time series heteroskedasticity 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • José M. Matías
    • 1
  • Manuel Febrero
    • 2
  • Wenceslao González-Manteiga
    • 2
  • Juan C. Reboredo
    • 3
  1. 1.Dpt. of StatisticsUniversity of VigoSpain
  2. 2.Dpt. of StatisticsUniversity of Santiago de CompostelaChile
  3. 3.Dpt. of Economic AnalysisUniversity of Santiago de CompostelaChile

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