FORECASTING THE EXCHANGE RATE SERIES WITH ANN: THE CASE OF TURKEY

Zaman serilerindeki hem doğrusal, hem de doğrusal olmayan yapıyı Yapay Sinir Ağlarıyla (YSA) modellemek mümkün olduğundan, YSA yönteminin doğrusal olmayan yapıya sahip kaotik serilerin modellenmesinde de kullanımı uygun olacaktır. Bu nedenle, yapılan çalışmada, yüksek dalgalanma gösteren Türkiye TL/US dolar döviz kuru zaman serisinin modellenmesinde YSA yöntemi kullanılmıştır. Elde edilen sonuçlar göre, YSA yönteminin mevsimsel ARIMA ve ARCH gibi modellerden daha iyi öngörüler ürettiği görülmüştür. Ek olarak, Türkiye döviz kuru zaman serisinin çözümlenmesinde, YSA yöntemi kullanımının detayları da verilmiştir.

FORECASTING THE EXCHANGE RATE SERIES WITH ANN: THE CASE OF TURKEY

As it is possible to model both linear and nonlinear structures in time series by using Artificial Neural Network (ANN), it is suitable to apply this method to the chaotic series having nonlinear component. Therefore, in this study, we propose to employ ANN method for high volatility Turkish TL/US dollar exchange rate series and the results show that ANN method has the best forecasting accuracy with respect to time series models, such as seasonal ARIMA and ARCH models. The suggestions about the details of the usage of ANN method are also made for the exchange rate of Turkey. 

___

  • Baharumshah, A.Z., & Liew, K.S. (2003). The predictability of ASEAN-5 exchange rates in the post-crisis era. Pertanika Journal of Social Science and Humanities, 11(1), 33-40.
  • Box, G., & Jenkins, G. (1976). Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
  • Bollerslev, T. (1986). Generalised Autoregressive Conditional Hetero-scedasticity. Journal of Econometrics, 51, 307-327.
  • Brooks, C. (1997). Linear and non-linear non forecastibility of high-frequency exchange rates. Journal of Forecasting, 16, 125-145.
  • Cichocki, A., & Unbehauen, R. (1993). Neural networks for optimization and signal processing. John Wiley & Sons, New York.
  • Cheung, Y.W. (1993). Long memory in foreign exchange-rates. Journal of Business and Economic Studies, 11, 93-101.
  • Clark, P.B., & McDonald, R. (1998). Exchange rates and economics fundamentals: A methodological comparison of BEEs and FEERs. (Working Paper, WP/98/67. Washington, International Monatary Fund).
  • Coakley, J., & Fuertes, A. (2001). Nonparametric cointegtration analysis of real exchange rates. Applied Financial Economics, 11, 1-8.
  • Cornell, B. (1977). Spot rates, forward rates and exchange market efficiency. Journal of Financial Economics, 5, 55-65.
  • Dornbusch, R. (1976). Expectations and exchange rates dynamics. Journal of Political Economy, 84(6), 1161- 1176.
  • Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimation of the variance of United Kingdom inflation. Econometrica, 50, 987-1007.
  • Egrioglu, E., Aladag, C.H., & Gunay, S. (2008). A new model selection strategy in artificial neural networks. Applied Mathematics and Computation, 195, 591–597.
  • Frankel, J. (1979). On the Mark: A theory of floating exchange rates based on real interest differantials. American Economic Review, 69(4), 610-622.
  • Franses, P.H., & Homelen, P.V. (1998). On forecasting exchange rates using neural Networks. Applied Financial Economics, 8, 589-596.
  • Gaynor, P.E., & Kirkpatrick, R.C. (1994). Introduction to time series modeling and forecasting in business and economics. Mc. Graw-Hill, Inc.
  • Giddy, I.H., & Duffey, G. (1975). The random behaviour of flexible exchange rates: implication for forecasting. Journal of International Business Studies, 6, 1-33.
  • Gardojevic, N., & Yang, J. (2000). The application of neural networks to exchange rate forecasting: The role of market microstructure variables. (Bank of Canada Working Paper 2000-23).
  • Hakkio, C.S., & Rush, M. (1986). Market efficiency and cointegration: An application to the Sterling and Deutschemark exchange market. Journal of International Money and Finance, 5, 221-230.
  • Hsieh, D.A. (1989a). Testing for nonlinear dependence in daily foreign exchange rate changes. Journal of Business, 62(3), 329-68.
  • Hsieh, D.A. (1989b). Modeling heteroscedasticity in daily foreign exchange rates. Journal of Business and Economic Statistics, 7(3), 307-317.
  • Hornik, K., Stinchcombe, M. & White, H. (1989). Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359-366.
  • Hu, M.Y., Zhang, G., Jiang, C.X., & Patuwo, B.E. (1999). A cross-validation analysis of neural network out-of- sample performance in exchange rate forecasting. Decision Sciences, 30(1), 197-216.
  • Lin, W.T., & Chen, Y.H. (1998). Forecasting foreign exchange rates with an intrinsically nonlinear dynamic speed of adjustment model. Applied Economics, 30, 295-312.
  • Ma, Y., & Kanas, A. (2000). Testing for a nonlinear relationship among fundamentals and exchange rates in ERM. Journal of International Money and Finance, 19, 135-152.
  • Mark, N. (1995). Exchange rates and fundamentals: evidence and long-run horizon predictability. American Economic Review, 85, 201-218.
  • Meese, M.A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies: do they fit out of sample?, Journal of International Economics, 14, 3-24.
  • Meese, R.A., & Rose, A.K. (1990). Nonlinear, nonparametric, nonessential exchange rate estimation. The American Economic Review, 80(2), 678-691.
  • Meese, R.A., & Rose, A.K. (1991). An empirical assessment of nonlinearities in models of exchange rates determination. Review of Economic Studies, 58(3), 613-19.
  • Palma, W., & Chan, N.H. (1997). Estimation and forecasting of long-memory processes with missing values. Journal of Forecasting, 16, 395-410.
  • Panda, C., & Narasimhan, V. (2007). Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling, 29, 227-236.
  • Parikh, A., & Williams, G. (1998). Modelling real exchange rate behaviour: A cross- country study. Applied Financial Economics, 8, 577-587.
  • Picton, P.D. (1994). Introduction to neural networks. Macmillan Press Ltd..
  • Plasmans, J., Verkooijen, W., & Daniels, H. (1998). Estimating structural exchange rate models by artificial neural networks. Applied Financial Economics, 8, 541-51.
  • Qi, M., & Zhang, G.P. (2001). An investigation of model selection criteria for neural network time series forecasting, European Journal of Operational Research, 132, 666-680.
  • Smith, K.A. (2002). Neural networks in business: Techniques and applications. Imprint Info Hershey: Idea Group.
  • Ture, M., & Kurt, I. (2006). Comparison of four different time series methods to forecast hepatitis: A virus infection, Expert Systems with Applications, 31, 41–46.
  • Zhang, G., & Hu, Y.M. (1998). Neural network forecasting of the British Pound/US Dollar exchange rate. International Journal of Management Science, 26(4), 495-506.
  • Zhang, G., Patuwo, B.E., & Hu, Y.M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 35-62.
  • Zurada, J.M. (1992). Introduction of artificial neural systems. St. Paul: West Publishing.