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Forecasting The Exchange Rate Series With Ann: The Case Of Turkey

Author

Listed:
  • Cem Kadilar

    (Hacettepe University)

  • Muammer Simsek

    (Cumhuriyet University)

  • Cagdas Hakan Aladag

    (Hacettepe University)

Abstract

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.

Suggested Citation

  • Cem Kadilar & Muammer Simsek & Cagdas Hakan Aladag, 2009. "Forecasting The Exchange Rate Series With Ann: The Case Of Turkey," Istanbul University Econometrics and Statistics e-Journal, Department of Econometrics, Faculty of Economics, Istanbul University, vol. 9(1), pages 17-29, May.
    • Handle: RePEc:ist:ancoec:v:9:y:2009:i:1:p:17-29
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    References listed on IDEAS

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    Cited by:

    1. Chen Jo-Hui & Diaz John Francis T., 2021. "Application of grey relational analysis and artificial neural networks on currency exchange-traded notes (ETNs)," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
    2. Yasemin Deniz Akarım, 2013. "A Comparison of Linear and Nonlinear Models in Forecasting Market Risk: The Evidence from Turkish Derivative Exchange," Journal of Economics and Behavioral Studies, AMH International, vol. 5(3), pages 164-172.
    3. Cagdas Hakan ALADAG & Miruna MAZURENCU MARINESCU, 2013. "Tl/Euro And Leu/Euro Exchange Rates Forecasting With Artificial Neural Network," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 2(2), pages 1-6, DECEMBER.
    4. CIOBANU Dumitru & BAR Mary Violeta, 2013. "On The Prediction Of Exchange Rate Dollar/Euro With An Svm Model," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 65(2), pages 91-109.
    5. Hatice Erkekoglu & Aweng Peter Majok Garang & Adire Simon Deng, 2020. "Comparative Evaluation of Forecast Accuracies for ARIMA, Exponential Smoothing and VAR," International Journal of Economics and Financial Issues, Econjournals, vol. 10(6), pages 206-216.
    6. Ganbold, Batzorig & Akram, Iqra & Fahrozi Lubis, Raisal, 2017. "Exchange rate volatility: A forecasting approach of using the ARCH family along with ARIMA SARIMA and semi-structural-SVAR in Turkey," MPRA Paper 84447, University Library of Munich, Germany, revised 2017.
    7. Cenk Ufuk Yıldıran & Abdurrahman Fettahoğlu, 2017. "Forecasting USDTRY rate by ARIMA method," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1335968-133, January.

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    More about this item

    Keywords

    Activation function; ARIMA; ARCH; Artificial neural network; Chaotic series; Exchange rate; Forecasting; Time series;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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