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On forecasting exchange rates using neural networks

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  • Philip Hans Franses
  • Paul van Homelen

Abstract

The paper considers the modelling, description and forecasting of four daily exchange rate returns relative to the Dutch guilder using artificial neural network models (ANNs). Based on simulations it is argued (i) that neglected GARCH does not lead to spuriously successful ANNs and (ii) that if there is some form of nonlinearity other than GARCH, ANNs will exploit this for improved forecasting. For the sample data it is found that ANNs do not yield favourable in-sample fits or forecasting performance. These results are interpreted as indicating that the nonlinearity often found in exchange rates is most likely due to GARCH and therefore ANNs are recommended as a diagnostic for mean nonlinearity.

Suggested Citation

  • Philip Hans Franses & Paul van Homelen, 1998. "On forecasting exchange rates using neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(6), pages 589-596.
    • Handle: RePEc:taf:apfiec:v:8:y:1998:i:6:p:589-596
    DOI: 10.1080/096031098332628
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    Cited by:

    1. Marcos Álvarez-Díaz & Alberto Álvarez, 2002. "Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos," Working Papers 0205, Universidade de Vigo, Departamento de Economía Aplicada.
    2. Adam Fadlalla & Farzaneh Amani, 2014. "Predicting Next Trading Day Closing Price Of Qatar Exchange Index Using Technical Indicators And Artificial Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 209-223, October.
    3. Rakesh K. Bissoondeeal & Michail Karoglou & Alicia M. Gazely, 2011. "Forecasting The Uk/Us Exchange Rate With Divisia Monetary Models And Neural Networks," Scottish Journal of Political Economy, Scottish Economic Society, vol. 58(1), pages 127-152, February.
    4. Stavros Degiannakis & Evdokia Xekalaki, 2007. "Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 149-171.
    5. Michael Dietrich, 2005. "Using simple neural networks to analyse firm activity," Working Papers 2005014, The University of Sheffield, Department of Economics, revised Jul 2005.
    6. Ahmad Zubaidi Baharumshah & Liew Khim Sen & Lim Kian Ping, 2003. "Exchange Rates Forecasting Model: An Alternative Estimation Procedure," International Finance 0307005, University Library of Munich, Germany.
    7. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, November.
    8. Teresa Aparicio & Dulce Saura, 2013. "Do Exchange Rate Series Present General Dependence? Some Results using Recurrence Quantification Analysis," Journal of Economics and Behavioral Studies, AMH International, vol. 5(10), pages 678-686.
    9. Dbouk, Wassim & Jamali, Ibrahim, 2018. "Predicting daily oil prices: Linear and non-linear models," Research in International Business and Finance, Elsevier, vol. 46(C), pages 149-165.
    10. Marcos Álvarez-Díaz & Alberto Álvarez, 2003. "Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos," Working Papers 0301, Universidade de Vigo, Departamento de Economía Aplicada.
    11. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    12. Cem Kadilar & Muammer Simsek & Cagdas Hakan Aladag, 2009. "Forecasting The Exchange Rate Series With Ann: The Case Of Turkey," Istanbul University Econometrics and Statistics e-Journal, Department of Econometrics, Faculty of Economics, Istanbul University, vol. 9(1), pages 17-29, May.
    13. Nathan Lael Joseph, 2003. "Using monthly returns to model conditional heteroscedasticity," Applied Economics, Taylor & Francis Journals, vol. 35(7), pages 791-801.
    14. Bou-Hamad, Imad & Jamali, Ibrahim, 2020. "Forecasting financial time-series using data mining models: A simulation study," Research in International Business and Finance, Elsevier, vol. 51(C).
    15. Jiří Trešl, 2011. "Srovnání vybraných metod predikce změn trendu indexu PX [Selected Methods of the Prediction of PX Index Trend Reversal]," Politická ekonomie, Prague University of Economics and Business, vol. 2011(2), pages 184-204.
    16. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    17. Marcos Álvarez-Díaz & Shawkat Hammoudeh & Rangan Gupta, 2013. "Detecting Predictable Non-linear Dynamics in Dow Jones Industrial Average and Dow Jones Islamic Market Indices using Nonparametric Regressions," Working Papers 201385, University of Pretoria, Department of Economics.
    18. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.

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