IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

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

Listed author(s):
  • 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)

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.mdpi.com/2227-9091/4/1/7/pdf
Download Restriction: no

File URL: http://www.mdpi.com/2227-9091/4/1/7/
Download Restriction: no

Article provided by MDPI, Open Access Journal in its journal Risks.

Volume (Year): 4 (2016)
Issue (Month): 1 (March)
Pages: 1-14

as
in new window

Handle: RePEc:gam:jrisks:v:4:y:2016:i:1:p:7-:d:65863
Contact details of provider: Web page: http://www.mdpi.com/

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. 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.
  2. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
  3. Kenneth Rogoff, 1996. "The Purchasing Power Parity Puzzle," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 647-668, June.
  4. Baillie, Richard T & Bollerslev, Tim, 1989. " Common Stochastic Trends in a System of Exchange Rates," Journal of Finance, American Finance Association, vol. 44(1), pages 167-181, March.
  5. 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.
  6. 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.
  7. 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.
  8. Chang, Chia-Lin & González-Serrano, Lydia & Jimenez-Martin, Juan-Angel, 2013. "Currency hedging strategies using dynamic multivariate GARCH," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 164-182.
  9. 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.
  10. Sarno,Lucio & Taylor,Mark P., 2003. "The Economics of Exchange Rates," Cambridge Books, Cambridge University Press, number 9780521485845, December.
  11. 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.
  12. 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.
  13. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
  14. 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..
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:4:y:2016:i:1:p:7-:d:65863. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.