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A Bayesian nonlinear support vector machine error correction model

Author

Listed:
  • Carine Brasseur

    (Fortis Bank Brussels, Belgium)

  • Marcelo Espinoza

    (Katholieke Universiteit Leuven, Belgium)

  • Johan A. K. Suykens

    (Katholieke Universiteit Leuven, Belgium)

  • Tony Van Gestel
  • Bart Baesens

    (School of Management, University of Southampton, UK)

  • Bart De Moor

    (Katholieke Universiteit Leuven, Belgium)

Abstract

The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using the idea of a kernel-based implicit nonlinear mapping to a high-dimensional feature space in which linear model formulations are specified. Practical expressions for the nonlinear regression are obtained in terms of the positive definite kernel function by solving a linear system. The nonlinear least squares support vector machine model is designed within the Bayesian evidence framework that allows us to find appropriate trade-offs between model complexity and in-sample model accuracy. From straightforward primal-dual reasoning, the Bayesian framework allows us to derive error bars on the prediction in a similar way as for linear models and to perform hyperparameter and input selection. Starting from the results of the linear modelling analysis, the Bayesian kernel-based prediction is successfully applied to out-of-sample prediction of an aggregated equity price index for the European chemical sector. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Carine Brasseur & Marcelo Espinoza & Johan A. K. Suykens & Tony Van Gestel & Bart Baesens & Bart De Moor, 2006. "A Bayesian nonlinear support vector machine error correction model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 77-100.
  • Handle: RePEc:jof:jforec:v:25:y:2006:i:2:p:77-100
    DOI: 10.1002/for.975
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    References listed on IDEAS

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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    2. Haoyuan, Shen & Yizhong, Ma & Chenglong, Lin & Jian, Zhou & Lijun, Liu, 2023. "Hierarchical Bayesian support vector regression with model parameter calibration for reliability modeling and prediction," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Zhenwei Li & Jing Han & Yuping Song, 2020. "On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1081-1097, November.

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