Mofid University The Journal of Economic Studies and Policies 2423-4648 0 11 2007 06 22 Forecasting Exchange Rates using Neural Networks and Wavelet Transformation Forecasting Exchange Rates using Neural Networks and Wavelet Transformation 19 42 47088 FA Hosain Abbasi Nejad Associate Professor, Faculty of Economics, University of Tehran Ahmad Mohammadi PhD student in Economics, Allameh Tabatabaei University Journal Article 2007 02 03 Forecasting currency exchange rates is an important financial problem that has received a great deal of attention especially because of its intrinsic difficulty and practical applications. The methods used for time series analyses are conventionally based on the concepts of stationarity and linearity. However, for cases in which the system dynamics are highly nonlinear, the performance of traditional models is very poor. On the other hand, artificial neural networks and wavelet  <br />transformation have demonstrated great potential for time series forecasting. Therefore in this thesis we propose a forecasting approach which combines the strengths of neural networks and wavelet transformation. In this approach the original exchange rates to be forecasted is first decomposed into various scale components using wavelet transformation. In the next step neural network techniques is applied for modeling components of the decomposed series. The final forecast of the original series is obtained by combining the components series forecasts. This approach is used for forecasting one-and ten-step ahead forecasts of daily exchange rates and its performance is compared whit those of ARIMA and neural network models. Results show that performance of the proposed method in two and five-step ahead forecasting is better as compared to those of other models. <br /><strong><em> </em></strong> Forecasting currency exchange rates is an important financial problem that has received a great deal of attention especially because of its intrinsic difficulty and practical applications. The methods used for time series analyses are conventionally based on the concepts of stationarity and linearity. However, for cases in which the system dynamics are highly nonlinear, the performance of traditional models is very poor. On the other hand, artificial neural networks and wavelet  <br />transformation have demonstrated great potential for time series forecasting. Therefore in this thesis we propose a forecasting approach which combines the strengths of neural networks and wavelet transformation. In this approach the original exchange rates to be forecasted is first decomposed into various scale components using wavelet transformation. In the next step neural network techniques is applied for modeling components of the decomposed series. The final forecast of the original series is obtained by combining the components series forecasts. This approach is used for forecasting one-and ten-step ahead forecasts of daily exchange rates and its performance is compared whit those of ARIMA and neural network models. Results show that performance of the proposed method in two and five-step ahead forecasting is better as compared to those of other models. <br /><strong><em> </em></strong> https://economic.mofidu.ac.ir/article_47088_31b291792c6140b04d1d5d5a8ae4bcb1.pdf