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Exchange Rates Forecasting and Trend Analysis from Machine Learning

Author(s)

Haiwen Huang1, Kexin Cui2

Affiliation(s)

1Business school, The University of Sydney, Australia 2College of Business & Public Management, Wenzhou-Kean University, China

Abstract

Exchange rates play a pivotal role in global economic and financial activities, influencing macroeconomic adjustments through nominal and real rates. Accurate exchange rate forecasting has become increasingly vital for investors, policymakers, and multinational enterprises, enabling effective trading strategies and proactive currency risk management. Despite the theoretical insights offered by fundamental models, their practical application in short-term forecasts remains limited. Statistical models like GARCH, ARIMA, ECM, and VAR have been widely utilized but struggle to capture the nonlinear dynamics and complex relationships in exchange rates, especially over extended forecasting horizons. Artificial intelligence (AI) models have demonstrated significant promise, although challenges like parameter optimization and overfitting persist. Recent empirical studies highlight the superior robustness of hybrid models over single-model approaches. Furthermore, volatility forecasting has gained importance for risk management, investment analysis, and policymaking. This study leverages high-frequency EUR/USD exchange rate data to evaluate minute-based volatility and assess the performance of various forecasting models, contributing to the advancement of predictive methodologies in currency markets.

Keywords

Exchange Rates; Machine Learning; LSTM; CEEMDAN

References

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