Exchange rate forecasting is an important and challenging task for both academic researchers and business practitioners. Several statistical or artificial intelligence approaches have been applied to forecasting exchange rate. The recent trend to improve the prediction accuracy is to combine individual forecasts in the form of the simple average or weighted average where the weight reflects the inverse of the prediction error. This kind of combination, however, does not reflect the current prediction error more than the relatively old performance. In this paper, we propose a new approach where the forecasting results of GARCH and neural networks are combined based on the weight reflecting the inverse of EWMA of the mean absolute percentage error (MAPE) of each individual prediction model. Empirical study results indicate that the proposed combining method has better accuracy than GARCH, neural networks, and traditional combining methods that utilize the MAPE for the weight.
|Number of pages||6|
|Journal||Neural Network World|
|Publication status||Published - 2005 Sep 19|
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Artificial Intelligence