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Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies

Author

Listed:
  • David E. Allen

    (The University of Sydney, The University of South Australia, Australia)

  • Michael McAleer

    (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University of Madrid, Spain)

  • Shelton Peiris

    (The University of Sydney, Australia)

  • Abhay K. Singh

    (Edith Cowan University, Australia)

Abstract

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive models, and additive nonlinear autoregressive models, plus Neural Network models.The results suggest that there is no dominating class of time series models, and the different currency pairs relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate returns data, from August 2005 to August 2015.

Suggested Citation

  • David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2015. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Tinbergen Institute Discussion Papers 15-125/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20150125
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    References listed on IDEAS

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    4. Taylor, Mark P & Peel, David A & Sarno, Lucio, 2001. "Nonlinear Mean-Reversion in Real Exchange Rates: Toward a Solution to the Purchasing Power Parity Puzzles," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(4), pages 1015-1042, November.
    5. Sarno,Lucio & Taylor,Mark P., 2003. "The Economics of Exchange Rates," Cambridge Books, Cambridge University Press, number 9780521485845, October.
    6. Engel, Charles & Hamilton, James D, 1990. "Long Swings in the Dollar: Are They in the Data and Do Markets Know It?," American Economic Review, American Economic Association, vol. 80(4), pages 689-713, September.
    7. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
    8. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
    9. Morana, Claudio & Beltratti, Andrea, 2004. "Structural change and long-range dependence in volatility of exchange rates: either, neither or both?," Journal of Empirical Finance, Elsevier, vol. 11(5), pages 629-658, December.
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    11. Meese, Richard A & Rose, Andrew K, 1990. "Nonlinear, Nonparametric, Nonessential Exchange Rate Estimation," American Economic Review, American Economic Association, vol. 80(2), pages 192-196, May.
    12. Sarno, Lucio & Taylor, Mark P. & Chowdhury, Ibrahim, 2004. "Nonlinear dynamics in deviations from the law of one price: a broad-based empirical study," Journal of International Money and Finance, Elsevier, vol. 23(1), pages 1-25, February.
    13. 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 347-364, Oct.-Dec..
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. End-of-Year Reading
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2015-12-23 01:57:00

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    Cited by:

    1. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
    2. Fatbardha Morina & Eglantina Hysa & Uğur Ergün & Mirela Panait & Marian Catalin Voica, 2020. "The Effect of Exchange Rate Volatility on Economic Growth: Case of the CEE Countries," JRFM, MDPI, vol. 13(8), pages 1-13, August.
    3. Angelos Kanas & Angelos Kotios & Panagiotis D. Zervopoulos, 2019. "Semi-parametric real exchange rates dynamics," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 643-656, February.
    4. Marinakis, Yorgos D. & White, Reilly & Walsh, Steven T., 2020. "Lotka–Volterra signals in ASEAN currency exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. Nicola Rubino, 2021. "In- and Out-of-Sample Performance of Nonlinear Models in International Price Differential Forecasting in a Commodity Country Framework," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 9(2), pages 107-127.

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    More about this item

    Keywords

    Non linear models; time series; non-parametric; smooth-transition regression models; neural networks; GMDH shell;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F3 - International Economics - - International Finance
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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