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Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos

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  • Marcos Álvarez-Díaz
  • Alberto Álvarez
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    Abstract

    It is widely proved the existence of non-linear deterministic structures in the exchange rates dynamic. In this work we intend to exploit these non-linear structures using forecasting methods such as Genetic Algorithm and Neural Networks in the specific case of the Yen/$ and British Pound/$ exchange rates. We also employ a novel perspective, called Data Fusion, based on the combination of the obtained results by the non-linear methods to verify if it exists a synergic effect which permits a predictive improvement. The analysis is performed considering both the point prediction and the devaluation or appreciation anticipation.

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    File URL: http://webs.uvigo.es/x06/sites/default/files/wp0301.pdf
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    Bibliographic Info

    Paper provided by Universidade de Vigo, Departamento de Economía Aplicada in its series Working Papers with number 0301.

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    Length: 38 pages
    Date of creation: Feb 2003
    Date of revision:
    Handle: RePEc:vig:wpaper:0301

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    Related research

    Keywords: Data Fusion; Genetic Algorithms; Neural Networks; Exchange Rates Forecasting;

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    1. Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
    2. Christopher J. Neely & Paul A. Weller & Robert Dittmar, 1997. "Is technical analysis in the foreign exchange market profitable? a genetic programming approach," Working Papers 1996-006, Federal Reserve Bank of St. Louis.
    3. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
    4. Fernandez-Rodriguez, Fernando & Sosvilla-Rivero, Simon, 1998. "Testing nonlinear forecastability in time series: Theory and evidence from the EMS," Economics Letters, Elsevier, vol. 59(1), pages 49-63, April.
    5. Colin Fyfe & John Paul Marney & Heather Tarbert, 1999. "Technical analysis versus market efficiency - a genetic programming approach," Applied Financial Economics, Taylor & Francis Journals, vol. 9(2), pages 183-191.
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    7. Soofi, Abdol S. & Cao, Liangyue, 1999. "Nonlinear deterministic forecasting of daily Peseta-Dollar exchange rate," Economics Letters, Elsevier, vol. 62(2), pages 175-180, February.
    8. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-64, Oct.-Dec..
    9. Barnett,William A. & Kirman,Alan P. & Salmon,Mark, 1997. "Nonlinear Dynamics and Economics," Cambridge Books, Cambridge University Press, number 9780521471411, October.
    10. Qi, Min, 1999. "Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 419-29, October.
    11. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-68, July.
    12. Racine, Jeffrey, 2001. "On the Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 380-82, July.
    13. Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
    14. Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.
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