TL/EURO AND LEU/EURO EXCHANGE RATES FORECASTING WITH ARTIFICIAL NEURAL NETWORK

Cagdas Hakan Aladag () and Miruna MAZURENCU Marinescu ()
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Cagdas Hakan Aladag: Hacettepe University, Faculty of Science, Department of Statistics, Beytepe, 06800, Ankara,Turkey
Miruna MAZURENCU Marinescu: The Bucharest University of Economic Studies, Department of Statistics and Econometrics, Bucharest, Romania

Journal of Social and Economic Statistics, 2013, vol. 2, issue 2, 1-6

Abstract: Forecasting is a popular research topic that is getting more and more attention from researchers and practitioners in various fields. It is a wellknown fact thatexchange rate forecasting is an important and challenging task for both academic researchers and business practitioners. Therefore, various approaches have been suggested in the literature for exchange rate forecasting. The forecasting techniques range from Box-Jenkins models to artificial networks. Artificial neural networks have also been successfully applied to various time series forecasting problems since they can model both linear and non-linear parts of time series. In addition, artificial neural networks method does not require assumptions such as those of other commonly used conventional methods. In this study, artificial neural networks are utilized to forecast TL/EUR and LEU/EUR exchange rates. In order to reach high forecasting accuracy level, different artificial neural networks models are examined and the obtained best results are compared to those produced by Box-Jenkins models

Keywords: Artificial neural networks; Box-Jenkins models; Exchange rates; Forecasting; Time series. (search for similar items in EconPapers)
JEL-codes: C45 C51 C53 F37 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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