Computational Economics

, Volume 53, Issue 2, pp 817–831 | Cite as

An Artificial Neural Network-Based Approach to the Monetary Model of Exchange Rate

  • Huseyin InceEmail author
  • Ali Fehim Cebeci
  • Salih Zeki Imamoglu


This paper aims to investigate the predictive accuracy of the flexible price monetary model of the exchange rate, estimated by an approach based on combining the vector autoregressive model and multilayer feedforward neural networks. The forecasting performance of this nonlinear, nonparametric model is analyzed comparatively with a monetary model estimated in a linear static framework; the monetary model estimated in a linear dynamic vector autoregressive framework; the monetary model estimated in a parametric nonlinear dynamic threshold vector autoregressive framework; and the naïve random walk model applied to six different exchange rates over three forecasting periods. The models are compared in terms of both the magnitude of their forecast errors and the economic value of their forecasts. The proposed model yielded promising outcomes by performing better than the random walk model in 16 out of 18 instances in terms of the root mean square error and 15 out of 18 instances in terms of mean return and Sharpe ratio. The model also performed better than linear models in 17 out of 18 instances for root mean square error and 14 out of 18 instances for mean returns and Sharpe ratio. The distinguishing feature of the proposed model versus the present models in the literature is its robustness to outperform the random walk model, regardless of whether the magnitude of forecast errors or the economic value of the forecasts is chosen as a performance measure.


Exchange rate forecasting Monetary model Artificial neural networks 


  1. Abhyankar, A., Sarno, L., & Valente, G. (2005). Exchange rates and fundamentals: Evidence on the economic value of predictability. Journal of International Economics, 66(2), 325–348. [Article].CrossRefGoogle Scholar
  2. Beckmann, J. (2013). Nonlinear exchange rate adjustment and the monetary model. Review of International Economics, 21(4), 654–670. Scholar
  3. Bilson, J. F. O. (1978). The monetary approach to the exchange rate: Some empirical evidence. IMF Staff Papers, 25, 48–75.CrossRefGoogle Scholar
  4. Bissoondeeal, R. K., Karoglou, M., & Gazely, A. M. (2011). Forecasting the UK/US exchange rate with divisia monetary models and neural networks. Scottish Journal of Political Economy, 58(1), 127–152. Scholar
  5. Frenkel, J. A. (1976). A monetary approach to the exchange rate: Doctrinal aspects and empirical evidence. Scandinavian Journal of Economics, 78, 200–224.CrossRefGoogle Scholar
  6. Hammerstrom, D. (1993). Neural networks at work. IEEE Spectrum, 30(6), 26–32. Scholar
  7. Haykin, S. S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  8. Hinton, G. E. (1992). How neural networks learn from experience. Scientific American, 267(3), 145–151.CrossRefGoogle Scholar
  9. Huber, F. (2016). Forecasting exchange rates using multivariate threshold models. The BE Journal of Macroeconomics, 16(1), 193–210. Scholar
  10. Joseph, N. L. (2001). Model specification and forecasting foreign exchange rates with vector autoregressions. Journal of Forecasting, 20(7), 451–484. Scholar
  11. Junttila, J., & Korhonen, M. (2011). Nonlinearity and time-variation in the monetary model of exchange rates. Journal of Macroeconomics, 33(2), 288–302. Scholar
  12. Khashei, M., Bijari, M., & Ardali, G. A. R. (2009). Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing, 72(4–6), 956–967. Scholar
  13. Khashei, M., Rafiei, F. M., & Bijari, M. (2013). Hybrid fuzzy auto-regressive integrated moving average (FARIMAH) model for forecasting the foreign exchange markets. International Journal of Computational Intelligence Systems, 6(5), 954–968. Scholar
  14. Kilian, L., & Taylor, M. P. (2003). Why is it so difficult to beat the random walk forecast of exchange rates? Journal of International Economics, 60(1), 85–107. Scholar
  15. Kim, B. H., Min, H. G., & Moh, Y. K. (2010). Nonlinear dynamics in exchange rate deviations from the monetary fundamentals: An empirical study. Economic Modelling, 27(5), 1167–1177. Scholar
  16. Leitch, G., & Tanner, J. E. (1991). Economic forecast evaluation profits versus the conventional error measures. American Economic Review, 81(3), 580–590.Google Scholar
  17. Leung, M. T., Chen, A. S., & Daouk, H. (2000). Forecasting exchange rates using general regression neural networks. Computers & Operations Research, 27(11–12), 1093–1110. Scholar
  18. Lisi, F., & Schiavo, R. A. (1999). A comparison between neural networks and chaotic models for exchange rate prediction. Computational Statistics & Data Analysis, 30(1), 87–102. Scholar
  19. Luk, K. C., Ball, J. E., & Sharma, A. (2001). An application of artificial neural networks for rainfall forecasting. Mathematical and Computer Modelling, 33(6–7), 683–693. Scholar
  20. Meese, R. A., & Rogoff, K. (1983). Empirical exchange-rate models of the seventies—Do they fit out of sample. Journal of International Economics, 14(1–2), 3–24. Scholar
  21. Moosa, I., & Burns, K. (2014). A reappraisal of the Meese–Rogoff puzzle. Applied Economics, 46(1), 30–40. Scholar
  22. Ni, H., & Yin, H. J. (2009). Exchange rate prediction using hybrid neural networks and trading indicators. Neurocomputing, 72(13–15), 2815–2823. Scholar
  23. Palmer, A., Montano, J. J., & Sese, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781–790. Scholar
  24. Qi, M., & Wu, Y. R. (2000). Exchange rates and fundamentals: Evidence from out-of-sample forecasting using neural networks. Computational Finance, 1999, 267–281.Google Scholar
  25. Sarno, L., Valente, G., & Wohar, M. E. (2004). Monetary fundamentals and exchange rate dynamics under different nominal regimes. Economic Inquiry, 42(2), 179–193. Scholar
  26. Taylor, M. P., & Peel, D. A. (2000). Nonlinear adjustment, long-run equilibrium and exchange rate fundamentals. Journal of International Money and Finance, 19(1), 33–53. Scholar
  27. Verkooijen, W. (1996). A neural network approach to long-run exchange rate prediction. Computational Economics, 9(1), 51–65.CrossRefGoogle Scholar
  28. Wu, B. (1995). Model-free forecasting for nonlinear time-series (with application to exchange-rates). Computational Statistics & Data Analysis, 19(4), 433–459. Scholar
  29. Wu, J. L., & Chen, S. L. (2001). Nominal exchange-rate prediction: Evidence from a nonlinear approach. Journal of International Money and Finance, 20(4), 521–532. Scholar
  30. Zhang, G. P. (2001). An investigation of neural networks for linear time-series forecasting. Computers & Operations Research, 28(12), 1183–1202. Scholar
  31. Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. Scholar
  32. Zhang, G. Q., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Huseyin Ince
    • 1
    Email author
  • Ali Fehim Cebeci
    • 2
  • Salih Zeki Imamoglu
    • 3
  1. 1.Department of Economics, Faculty of Business AdministrationGebze Technical UniversityGebzeTurkey
  2. 2.Department of Economics, Faculty of Business AdministrationGebze Technical UniversityGebzeTurkey
  3. 3.Science and Technology Studies, Faculty of Business AdministrationGebze Technical UniversityGebzeTurkey

Personalised recommendations