Research Article
BibTex RIS Cite

An Application Of Exchange Rate Forecasting In Turkey

Year 2011, Volume: 24 Issue: 4, 817 - 828, 16.12.2011

Abstract

In this study, exchange rate forecasting is studied which plays a key role in free market systems. Official daily data of Central Bank of The Republic of Turkey (CBRT) are used for USD/TL ($/TL), EURO/TL (€/TL) and POUND/TL (£/TL) pars. Moving averages (MA) method, single exponential smoothing method, Holt’s method,Winter’s method and ARIMA models are applied to the each pars Performance of the models are assessed with the performance criteria of mean absolute percentage error (MAPE), root mean square errors (RMSE) and mean square error (MAE). As a result of study, successfully application of the methods based on trend analysis is exhibited for exchange rates in Turkey. These methods are evaluated according to MAPE, RMSE and MAE criteria and the best results are obtained by Winter’s method which means that Winter’s method is an useful method to forecast exchange rates for the given time interval in Turkey.


References

  • [1] Leu, Y., Lee, C-P. and Jou, Y-Z., “A distancebased fuzzy time series model for exchange rates forecasting”, Expert Systems with Applications, 36: 8107-8114 (2009).
  • [2] Panda, C., Narasimhan, V., “Forecasting exchange rate better with artificial neural network”, Journal of Policy Modeling, 29: 227- 236 (2007). , [3] Ince, H., Trafalis, T. B., “A hybrid model for exchange rate prediction”, Decision Support Systems, 42: 1054–1062 (2006).
  • [4] Box, G. E. P., Jenkins, G. M., Time series analysis: forecasting and control, Holden-Day San Francisco (1976).
  • [5] Chu, F.-L., “Forecasting tourism demand with ARMA-based methods”, Tourism Management, 30: 740-751 (2009).
  • [6] Chu, F.-L., “Analyzing and forecasting tourism demand with ARAR algorithm”, Tourism Management, 29: 1185-1196 (2008).
  • [7] Chang, C.-L., Sriboonchitta, S., Wiboonpongse, A., “Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation”, Mathematics and Computers in Simulation, 79: 1730-1744 (2009).
  • [8] Ediger, V.Ş., Akar, S., Uğurlu, B., “Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model”, Energy Policy, 34: 3836-3846 (2006).
  • [9] Lee, C.-K., Song, H.-J., Mjelde, J. M., “The forecasting of International Expo tourism using quantitative and qualitative techniques”, Tourism Management, 29: 1084-1098 (2008).
  • [10] Önder, E., Hasgül, Ö., “Yabancı ziyaretçi sayısının tahmininde Box-Jenkins modeli, Winters yöntemi ve yapay sinir ağlarıyla zaman serisi analizi”, İstanbul Üniversitesi İşletme Fakültesi, İşletme İktisadi Enstitüsü DergisiYönetim Dergisi, 62: Şubat (2009).
  • [11] Ediger, V. Ş., Akar, S., “ARIMA forecasting of primary energy demand by fuel in Turkey”, Energy Policy, 35: 1701-1708 (2007).
  • [12] Engle, R.F., “Autoregressive conditional heteroscedasticity with estimates of the variance of the United Kingdom inflation”, Econometrica, 50: 987-1008 (1982).
  • [13] Bollerslev, T., “Generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics 31: 307–327 (1986).
  • [14] Gençay, R., “Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules”, Journal of International Economics, 47: 91–107 (1999).
  • [15] Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O., “Fractionally integrated generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, 74: 3 –30 (1996).
  • [16] Bollerslev, T., & Wright, J. H., “Highfrequency data, frequency domain inference, and volatility forecasting”, Review of Economics and Statistics, 83: 596– 602 (2001).
  • [17] Gooijer, J. G. De, Hyndman, R. J., “25 years of time series forecasting”, International Journal of Forecasting, 22: 443–473 (2006).
  • [18] El Shazy, M. R., El Shazy, H. E., “Comparing the forecasting performance of neural networks and forward exchange rates”, Journal of Multinational Financial Management, 7: 345- 356 (1997).
  • [19] Demirbaş, S., “Cointegration analysis-causality testing and Wagner’s Law: the case of Turkey, 1950–1990”, Annual Meeting of the European Public Choice Society, April 7-10: (1999).
  • [20] Brown, R. G., Smoothing, Forecasting and prediction of discrete time series, Englewood Cliffs, NJ7 Prentice-Hall (1963).
  • [21] Holt, C.C., “Forecasting seasonals and trends by exponentially weighted moving averages”, International Journal of Forecasting, 20: 5–13 (2004).
  • [22] Winters, P. R., “Forecasting sales by exponentially weighted moving averages”, Management Science, 6: 324–342 (1960).
  • [23] Blanchard, M., Desrochers, G., “Generation of autocorrelated wind speeds for wind energy conversion system studies”, Solar Energy, 33: 571–579 (1984).
  • [24] Brown, B.G., Katz, R.W., Murphy, A.A., “Time series models to simulate and forecast wind speed and wind power”, Journal of Applied Meteorology, 23: 1184-1195 (1984).
  • [25] Kamal, L., Jafri, Y.Z., “Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan”, Solar Energy, 61 (1): 23-32 (1997).
  • [26] Ho, S.L., Xie, M., “The use of ARIMA models for reliability forecasting and analysis”, Computers Industrial Engineering, 35 (1-2): 213-216 (1998).
  • [27] Saab, S., Badr, E., Nasr, G., “Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon”, Energy, 26: 1-14 (2001).
  • [28] Zhang, G.P., “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing, 50: 159-175 (2001).
  • [29] Ho, S.L., Xie, M., Goh, T.N., “A comparative study of neural network and Box-Jenkins ARIMA modeling in time series predictions”, Computers and Industrial Engineering, 42: 371-375 (2002).
  • [30] Wilson, J.H., Keating, B., Business Forecasting, Richard D Irwin Inc. USA 1994.
  • [31] Lim, C., McAleer, M., “A Monthly seasonal variations in Asian tourism to Australia”, Annals of Tourism Researc,h 28: 68–82 (2002).
  • [32] Lim, C., McAleer, M., “Time series forecast of international travel demand for Australia”, Tourism Management, 23: 389–396 (2002).
  • [33] Song, H., Li, G., “Tourism demand modelling and forecasting-A review of recent research”, Tourism Management, 29: 203-220 (2008).
  • [34] Witt, S. F., & Witt, C. A., “Tourism forecasting: error magnitude, direction of change of error and trend error”, Journal of Travel Research, 30(2): 26-33 (1991).
  • [35] Çuhadar, M., et al, “Turizm talebinin yapay sinir ağları ile tahmini ve zaman serisi yöntemleri ile karşılaştırmalı analizi: Antalya iline yönelik bir uygulama”, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Dergisi, 14: 99-114 (2009).
  • [36] Lewis, C. D., Industrial and Business Forecasting Methods, London Butterworths (1982).
  • [37] Preminger, A., Franck, R., “Forecasting exchange rates: A robust regression approach”, International Journal of Forecasting, 23: 71– 84 (2007).
Year 2011, Volume: 24 Issue: 4, 817 - 828, 16.12.2011

Abstract

References

  • [1] Leu, Y., Lee, C-P. and Jou, Y-Z., “A distancebased fuzzy time series model for exchange rates forecasting”, Expert Systems with Applications, 36: 8107-8114 (2009).
  • [2] Panda, C., Narasimhan, V., “Forecasting exchange rate better with artificial neural network”, Journal of Policy Modeling, 29: 227- 236 (2007). , [3] Ince, H., Trafalis, T. B., “A hybrid model for exchange rate prediction”, Decision Support Systems, 42: 1054–1062 (2006).
  • [4] Box, G. E. P., Jenkins, G. M., Time series analysis: forecasting and control, Holden-Day San Francisco (1976).
  • [5] Chu, F.-L., “Forecasting tourism demand with ARMA-based methods”, Tourism Management, 30: 740-751 (2009).
  • [6] Chu, F.-L., “Analyzing and forecasting tourism demand with ARAR algorithm”, Tourism Management, 29: 1185-1196 (2008).
  • [7] Chang, C.-L., Sriboonchitta, S., Wiboonpongse, A., “Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation”, Mathematics and Computers in Simulation, 79: 1730-1744 (2009).
  • [8] Ediger, V.Ş., Akar, S., Uğurlu, B., “Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model”, Energy Policy, 34: 3836-3846 (2006).
  • [9] Lee, C.-K., Song, H.-J., Mjelde, J. M., “The forecasting of International Expo tourism using quantitative and qualitative techniques”, Tourism Management, 29: 1084-1098 (2008).
  • [10] Önder, E., Hasgül, Ö., “Yabancı ziyaretçi sayısının tahmininde Box-Jenkins modeli, Winters yöntemi ve yapay sinir ağlarıyla zaman serisi analizi”, İstanbul Üniversitesi İşletme Fakültesi, İşletme İktisadi Enstitüsü DergisiYönetim Dergisi, 62: Şubat (2009).
  • [11] Ediger, V. Ş., Akar, S., “ARIMA forecasting of primary energy demand by fuel in Turkey”, Energy Policy, 35: 1701-1708 (2007).
  • [12] Engle, R.F., “Autoregressive conditional heteroscedasticity with estimates of the variance of the United Kingdom inflation”, Econometrica, 50: 987-1008 (1982).
  • [13] Bollerslev, T., “Generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics 31: 307–327 (1986).
  • [14] Gençay, R., “Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules”, Journal of International Economics, 47: 91–107 (1999).
  • [15] Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O., “Fractionally integrated generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, 74: 3 –30 (1996).
  • [16] Bollerslev, T., & Wright, J. H., “Highfrequency data, frequency domain inference, and volatility forecasting”, Review of Economics and Statistics, 83: 596– 602 (2001).
  • [17] Gooijer, J. G. De, Hyndman, R. J., “25 years of time series forecasting”, International Journal of Forecasting, 22: 443–473 (2006).
  • [18] El Shazy, M. R., El Shazy, H. E., “Comparing the forecasting performance of neural networks and forward exchange rates”, Journal of Multinational Financial Management, 7: 345- 356 (1997).
  • [19] Demirbaş, S., “Cointegration analysis-causality testing and Wagner’s Law: the case of Turkey, 1950–1990”, Annual Meeting of the European Public Choice Society, April 7-10: (1999).
  • [20] Brown, R. G., Smoothing, Forecasting and prediction of discrete time series, Englewood Cliffs, NJ7 Prentice-Hall (1963).
  • [21] Holt, C.C., “Forecasting seasonals and trends by exponentially weighted moving averages”, International Journal of Forecasting, 20: 5–13 (2004).
  • [22] Winters, P. R., “Forecasting sales by exponentially weighted moving averages”, Management Science, 6: 324–342 (1960).
  • [23] Blanchard, M., Desrochers, G., “Generation of autocorrelated wind speeds for wind energy conversion system studies”, Solar Energy, 33: 571–579 (1984).
  • [24] Brown, B.G., Katz, R.W., Murphy, A.A., “Time series models to simulate and forecast wind speed and wind power”, Journal of Applied Meteorology, 23: 1184-1195 (1984).
  • [25] Kamal, L., Jafri, Y.Z., “Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan”, Solar Energy, 61 (1): 23-32 (1997).
  • [26] Ho, S.L., Xie, M., “The use of ARIMA models for reliability forecasting and analysis”, Computers Industrial Engineering, 35 (1-2): 213-216 (1998).
  • [27] Saab, S., Badr, E., Nasr, G., “Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon”, Energy, 26: 1-14 (2001).
  • [28] Zhang, G.P., “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing, 50: 159-175 (2001).
  • [29] Ho, S.L., Xie, M., Goh, T.N., “A comparative study of neural network and Box-Jenkins ARIMA modeling in time series predictions”, Computers and Industrial Engineering, 42: 371-375 (2002).
  • [30] Wilson, J.H., Keating, B., Business Forecasting, Richard D Irwin Inc. USA 1994.
  • [31] Lim, C., McAleer, M., “A Monthly seasonal variations in Asian tourism to Australia”, Annals of Tourism Researc,h 28: 68–82 (2002).
  • [32] Lim, C., McAleer, M., “Time series forecast of international travel demand for Australia”, Tourism Management, 23: 389–396 (2002).
  • [33] Song, H., Li, G., “Tourism demand modelling and forecasting-A review of recent research”, Tourism Management, 29: 203-220 (2008).
  • [34] Witt, S. F., & Witt, C. A., “Tourism forecasting: error magnitude, direction of change of error and trend error”, Journal of Travel Research, 30(2): 26-33 (1991).
  • [35] Çuhadar, M., et al, “Turizm talebinin yapay sinir ağları ile tahmini ve zaman serisi yöntemleri ile karşılaştırmalı analizi: Antalya iline yönelik bir uygulama”, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Dergisi, 14: 99-114 (2009).
  • [36] Lewis, C. D., Industrial and Business Forecasting Methods, London Butterworths (1982).
  • [37] Preminger, A., Franck, R., “Forecasting exchange rates: A robust regression approach”, International Journal of Forecasting, 23: 71– 84 (2007).
There are 36 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Industrial Engineering
Authors

Aykan Akıncılar

İzzettin Temiz This is me

Erol Şahin

Publication Date December 16, 2011
Published in Issue Year 2011 Volume: 24 Issue: 4

Cite

APA Akıncılar, A., Temiz, İ., & Şahin, E. (2011). An Application Of Exchange Rate Forecasting In Turkey. Gazi University Journal of Science, 24(4), 817-828.
AMA Akıncılar A, Temiz İ, Şahin E. An Application Of Exchange Rate Forecasting In Turkey. Gazi University Journal of Science. December 2011;24(4):817-828.
Chicago Akıncılar, Aykan, İzzettin Temiz, and Erol Şahin. “An Application Of Exchange Rate Forecasting In Turkey”. Gazi University Journal of Science 24, no. 4 (December 2011): 817-28.
EndNote Akıncılar A, Temiz İ, Şahin E (December 1, 2011) An Application Of Exchange Rate Forecasting In Turkey. Gazi University Journal of Science 24 4 817–828.
IEEE A. Akıncılar, İ. Temiz, and E. Şahin, “An Application Of Exchange Rate Forecasting In Turkey”, Gazi University Journal of Science, vol. 24, no. 4, pp. 817–828, 2011.
ISNAD Akıncılar, Aykan et al. “An Application Of Exchange Rate Forecasting In Turkey”. Gazi University Journal of Science 24/4 (December 2011), 817-828.
JAMA Akıncılar A, Temiz İ, Şahin E. An Application Of Exchange Rate Forecasting In Turkey. Gazi University Journal of Science. 2011;24:817–828.
MLA Akıncılar, Aykan et al. “An Application Of Exchange Rate Forecasting In Turkey”. Gazi University Journal of Science, vol. 24, no. 4, 2011, pp. 817-28.
Vancouver Akıncılar A, Temiz İ, Şahin E. An Application Of Exchange Rate Forecasting In Turkey. Gazi University Journal of Science. 2011;24(4):817-28.