Forecasting Exchange Rate Dynamics: A Comparative Study of Traditional Econometric Models and Machine Learning Models

Abstract

Accurate exchange rate forecasting is crucial for economic policy, risk management, and financial decision-making. This study compares traditional hybrid econometric models, specifically the Autoregressive Integrated Moving Average with Exogenous Variables and Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH), with machine learning approaches, including Random Forest, Multi-Layer Perceptron, and Long Short-Term Memory networks, to predict the South African Rand (ZAR) against major global currencies: the United States Dollar (USD), Euro (EUR), British Pound (GBP), Japanese Yen (JPY), and Chinese Yuan (CNY). Using daily exchange rate data from 2000 to 2024, model performance is evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (sMAPE), and Mean Directional Accuracy (MDA). The results indicate that no single model consistently outperforms across all currency pairs. The ARIMAX-GARCH model excels in trend prediction, the Random Forest model balances predictive accuracy and adaptability, the Multi- Layer Perceptron model minimizes absolute errors but struggles with directional accuracy, and the Long Short-Term Memory model captures long-term dependencies but underperforms in volatile markets. These findings highlight the need for hybrid forecasting models that integrate machine learning and econometric techniques while incorporating macroeconomic indicators to enhance predictive reliability.