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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

    () (School of Mathematics and Statistics, the University of Sydney, Sydney, NSW 2006, Australia
    Centre for Applied Financial Studies, School of Business, the University of South Australia, Sydney, SA 5001, Australia)

  • Michael McAleer

    () (Department of Quantitative Finance, National Tsing Hua University, Taichung 402, Taiwan
    Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam 3000, The Netherlands
    Tinbergen Institute, Rotterdam 3000, The Netherlands
    Department of Quantitative Economics, Complutense University of Madrid, Madrid 28223, Spain)

  • Shelton Peiris

    () (School of Mathematics and Statistics, the University of Sydney, Sydney, NSW 2006, Australia)

  • Abhay K. Singh

    () (School of Accounting, Finance and Economics, Edith Cowan University, Perth, WA 6027, 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 models are evaluated on the basis of error metrics for twenty day out-of-sample forecasts using the mean average percentage errors (MAPE). 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, 2016. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Risks, MDPI, Open Access Journal, vol. 4(1), pages 1-14, March.
  • Handle: RePEc:gam:jrisks:v:4:y:2016:i:1:p:7-:d:65863
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    non linear models; time series; non-parametric; smooth-transition regression models; neural networks; GMDH shell;

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting
    • K2 - Law and Economics - - Regulation and Business Law

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