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Prediction in chaotic time series: methods and comparisons with an application to financial intra-day data

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  • D. Guegan
  • L. Mercier

Abstract

Different prediction methods for chaotic deterministic systems are compared. Two methods of reconstructing the dynamics of the systems are considered with a view to producing a profitable trading model. The methods developed are the 'nearest neighbours' method and the 'radial basis functions' method. The optimal prediction horizon according to the sampling time step, and a reliable method to measure the prediction error are discussed. These methods are applied to the intra-day series of exchange rates, namely DEM/FRF. Developments concerning the importance of noise when chaotic systems are studied are provided.

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  • D. Guegan & L. Mercier, 2005. "Prediction in chaotic time series: methods and comparisons with an application to financial intra-day data," The European Journal of Finance, Taylor & Francis Journals, vol. 11(2), pages 137-150.
    • Handle: RePEc:taf:eurjfi:v:11:y:2005:i:2:p:137-150
    DOI: 10.1080/13518470110074846
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    References listed on IDEAS

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    1. Bisaglia, Luisa & Guegan, Dominique, 1998. "A comparison of techniques of estimation in long-memory processes," Computational Statistics & Data Analysis, Elsevier, vol. 27(1), pages 61-81, March.
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    5. repec:dau:papers:123456789/12729 is not listed on IDEAS
    6. Dominique Guegan & Mercier Ludovic, 1997. "prediction in chaotic time series: methods and comparisons using simulations," Post-Print halshs-00375663, HAL.
    7. Weigend, Andreas S. & Lebaron, Blake, 1994. "Evaluating Neural Network Predictors by Bootstrapping," SFB 373 Discussion Papers 1994,35, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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    Citations

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    Cited by:

    1. Ayşe İşi & Fatih Çemrek, 2019. "Comparison of the Global, Local and Semi-Local Chaotic Prediction Methods for Stock Markets: The Case of FTSE-100 Index," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 7(2), pages 289-300, December.
    2. Rachida Hennani & Michel Terraza, 2015. "Contributions of a noisy chaotic model to the stressed Value-at-Risk," Economics Bulletin, AccessEcon, vol. 35(2), pages 1262-1273.
    3. Dominique Guegan, 2009. "Chaos in economics and finance," Post-Print halshs-00187885, HAL.
    4. Dominique Guegan, 2009. "Chaos in Economics and Finance," Post-Print halshs-00375713, HAL.
    5. Rachida Hennani, 2015. "Can the Lasota(1977)’s model compete with the Mackey-Glass(1977)’s model in nonlinear modelling of financial time series?," Working Papers 15-09, LAMETA, Universtiy of Montpellier, revised Jun 2015.
    6. Dominique Guegan, 2009. "Chaos in Economics and Finance," PSE-Ecole d'économie de Paris (Postprint) halshs-00375713, HAL.

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