A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting
Journal Title: International Journal of Academic Research in Business and Social Sciences - Year 2016, Vol 6, Issue 1
Abstract
Modeling and forecasting of dynamics nominal exchange rate has long been a focus of financial and economic research. Artificial Intelligence (IA) modeling has recently attracted much attention as a new technique in economic and financial forecasting. This paper proposes an alternative approach based on artificial neural network (ANN) to predict the daily exchange rates. Our empirical study is based on a series of daily data in Tunisia. In order to evaluate this approach, we compare it with a generalized autoregressive conditional heteroskedasticity (GARCH) model in terms of their performance. Results indicate that the proposed nonlinear autoregressive (NAR) model is an accurate and a quick prediction method. This finding helps businesses and policymakers to plan more appropriately.
Authors and Affiliations
Fahima Charef, Fethi Ayachi
Keywords
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- EP ID EP183698
- DOI 10.6007/IJARBSS/v6-i1/1993
- Views 138
- Downloads 0
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