Forecasting foreign exchange rate volatility using deep learning: Case of US dollar/Algerian dinar during the COVID-19 pandemic - Naas, Meryem-Nadjat; Zouaoui, Habib (Research Papers in Economics and Finance, 2024)
This study explores the application of deep learning techniques in forecasting foreign exchange rate volatility, leveraging the capabilities of neural networks to capture complex patterns and nonlinear relationships within financial data. We applied the auto regressive integrated moving average (ARIMA) and machine learning linear regression (LR) model, deep learning models ( recurrent neural networks (RNN), bidirectional LSTM (Bi-LSTM), long short-term memory (LSTM) and gated recurrent unit (GRU). In terms of forecasting errors, and Python routines were used for such purpose. Morever, In order to investigate the quality of the models used, we compared the performances of these algorithms in US dollar/algerian dinar exchange rate forecasting througt the application of significance satistical tests (R-squared, MSE, RMSE, MAE, MAPE)The results clearly depict that contemporary techniques have been shown to produce more accurate results than conventional regression-based modeling. The machine learning linear regression (LR) model provides the maximum accuracy rate of (99.83%) followed by the RNN models with GRU model (92.27%) , Bi-LSTM model (87.34%), LSTM model (74.68%) and ARIMA model (32.29%) .