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Estimating structural exchange rate models by artificial neural networks

Author

Listed:
  • Joseph Plasmans
  • William Verkooijen
  • Hennie Daniels

Abstract

No theory of structural exchange rate determination has yet been found that performs well in prediction experiments. Only very seldom has the simple random walk model been significantly outperformed. Referring to three, sometimes highly nonlinear, monetary and nonmonetary structural exchange rate models, a feedforward artificial neural network specification is investigated to determine whether it improves the prediction performance of structural and random walk exchange rate models. A new test for univariate nonlinear cointegration is also derived. Important nonlinearities are not detected for monthly data of US dollar rates in Deutsche marks, Dutch guilders, British pounds and Japanese yens.

Suggested Citation

  • Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
  • Handle: RePEc:taf:apfiec:v:8:y:1998:i:5:p:541-551
    DOI: 10.1080/096031098332844
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    References listed on IDEAS

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    1. Verkooijen,W. & Plasmans,J. & Daniels,H., 1995. "Long-run Exchange Rate Determination:a Neural Network Study," Papers 95109, Tilburg - Center for Economic Research.
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    Cited by:

    1. 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.
    2. Chun-Teck Lye & Tze-Haw Chan & Chee-Wooi Hooy, 2012. "Nonlinear Analysis Of Chinese And Malaysian Exchange Rates Predictability With Monetary Fundamentals," Journal of Global Business and Economics, Global Research Agency, vol. 5(1), pages 38-49, July.
    3. Boldea, O. & Engwerda, J.C. & Michalak, T. & Plasmans, J.E.J. & Salmah, S., 2011. "A Simulation Study of an ASEAN Monetary Union (Replaces CentER DP 2010-100)," Discussion Paper 2011-098, Tilburg University, Center for Economic Research.
    4. Jo-Hui Chen & Yen-Po Fang, 2011. "A study on the modified components of Asian Currency Unit: an application of the Artificial Neural Network," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(2), pages 329-347, February.
    5. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
    6. Alper Özün, 2006. "Using New Information Technologies for Modelling Data on Global Markets: An Efficient Interaction between "Artificial" Human Brain and Economics," Papers of the Annual IUE-SUNY Cortland Conference in Economics,in: Proceedings of the Conference on Human and Economic Resources, pages 349-359 Izmir University of Economics.
    7. Ahmad Zubaidi Baharumshah & Liew Khim Sen & Lim Kian Ping, 2003. "Exchange Rates Forecasting Model: An Alternative Estimation Procedure," International Finance 0307005, EconWPA.
    8. Poghosyan, Karen & Boldea, Otilia, 2013. "Structural versus matching estimation: Transmission mechanisms in Armenia," Economic Modelling, Elsevier, vol. 30(C), pages 136-148.
    9. Jing Yang & Nikola Gradojevic, 2006. "Non-linear, non-parametric, non-fundamental exchange rate forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(4), pages 227-245.
    10. Chun-Teck Lye & Tze-Haw Chan & Chee-Wooi Hooy, 2011. "Nonlinear prediction of Malaysian exchange rate with monetary fundamentals," Economics Bulletin, AccessEcon, vol. 31(3), pages 1960-1967.
    11. Nikola Gradojevic & Jing Yang, 2000. "The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables," Staff Working Papers 00-23, Bank of Canada.
    12. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    13. 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.
    14. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    15. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    16. Panda, Chakradhara & Narasimhan, V., 2007. "Forecasting exchange rate better with artificial neural network," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 227-236.
    17. Engwerda, J. & Boldea, O. & Michalak, T. & Plasmans, J. & Salmah,, 2012. "A simulation study of an ASEAN monetary union," Economic Modelling, Elsevier, vol. 29(5), pages 1870-1890.
    18. 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.
    19. Stelios Bekiros, 2007. "A neurofuzzy model for stock market trading," Applied Economics Letters, Taylor & Francis Journals, vol. 14(1), pages 53-57.

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