The forming and forecasting of supposed exchange rate dynamics has long existed a focus of financial and financial studies. Artificial intelligence (AI) modeling has currently received greatly of attention as a new method in business-related and financial predicting. This research suggests an alternate blueprint for forecasting regularly exchange rates that is based on affected neural network (ANN).Our practical research is based on a set of Tunisian everyday data. In order to judge this strategy, we compare allure performance to that of a statement autoregressive conditional heteroskedasticity (GARCH) model. The results show that the projected nonlinear autoregressive (NAR) model is a reliable and fast prediction means. This discovery admits businesses and policymakers to plan in a more excellent manner.
Author(s) Details:
Fahima Charef,
Department of Finance, FSEGT, University of
Tunis Elmanar, Tunis, Tunisia.
Fethi
Ayachi,
Department
of economic, High School of Economics and Trade of Tunis, CEMAFI, Nice, France.
Please see the link here: https://stm.bookpi.org/CTBEF-V2/article/view/9790
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