A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting
Fahima Charef and
Fethi Ayachi
International Journal of Academic Research in Business and Social Sciences, 2016, vol. 6, issue 1, 244-253
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.
Keywords: Nominal exchange rate; Neural Networks; GARCH model; Forecasting; TunisiaJournal: International Journal of Academic Research in Business and Social Sciences (search for similar items in EconPapers)
JEL-codes: C45 C53 F31 (search for similar items in EconPapers)
Date: 2016
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Journal Article: A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:hur:ijarbs:v:6:y:2016:i:1:p:244-253
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