No 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 newtechnique 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.

Purpose The purpose of this paper is to investigate the dynamic relationship between inflation, interest rate differential, the exchange trade and exchange rate parities, i.e. (USD/TND, EUR/TND and JPY/TND). Design/methodology/approach Given the existing non-linear form between the different time series in this study, the empirical analysis is based on the using of non-parametric method such as the artificial neural networks. In order to detect the causality relationship between the variables, the authors use an NARX model. Findings Mixed results were found; there is a bidirectional relationship between inflation and exchange rate among others. Results also show that there is a strong correlation between the terms of trade and inflation, which says that trade openness increases the demand for imported goods and, therefore, causes more inflation for Tunisia. Originality/value After these results, it is important for policymakers to know which factors influence exchange rate stability, especially in developing countries like Tunisia.

No abstract