Modeling exchange rates using wavelet decomposed genetic neural networks

Exchange rate forecasting has always been a challenging area of research to applied statisticians and econometricians. In this paper, we propose to use a methodology that combines artificial intelligence modeling techniques with wavelet multiresolution methodology for forecasting of daily spot foreign exchange rates of major internationally traded currencies. The original exchange rate series to be modeled is first decomposed into various frequency resolution related components using wavelet-filtering techniques. The decomposed components represent various components of the original series, like the long-term trend and the periodic components at different periodicities. Artificial intelligence modeling, using genetically optimized neural networks, is applied for modeling components of the decomposed series. The final forecast of the original series is obtained by combining the component series forecasts. In the empirical studies, we apply the proposed modeling technique for forecasting one- and multi-step ahead forecasts of spot exchange rates. Results of the empirical study indicate superior performance of the proposed technique as compared to the traditional exchange rate forecasting models.