AI: Identifies Currency Risks Before They Hit the Bottom Line
1 viewsAbstract
Foreign exchange (FX) markets are increasingly characterized by non-linear volatility driven by geopolitical shifts, inflationary cycles, and the emergence of digital assets. This study evaluates the effectiveness of Artificial Intelligence (AI) in forecasting currency risk compared to traditional econometric benchmarks (Random Walk, ARIMA, GARCH). Using a quantitative- dominant mixed-methods approach, the research analyzes a 13-year dataset (2010–2023) across 10 major and emerging-market currency pairs, complemented by a survey of 45 global risk professionals. The findings reveal that Deep Learning models, specifically Long Short-Term Memory (LSTM) and Transformer networks, reduced forecasting errors (RMSE) by 48% to 51% across all currency pairs. In classifying currency crises, XGBoost models correctly identified stress episodes— including the 2021 Turkish Lira and 2023 Argentine Peso devaluations—an average of 18.4 trading days before crossing volatility thresholds. Furthermore, cointegration analysis identifies a robust long-run equilibrium between currency depreciation and gold during high-inflation regimes, whereas Bitcoin serves as a regime-specific hedge during periods of capital control. Despite a significantly higher Sharpe ratio (1.47) for AI-enhanced strategies, 84% of practitioners cite model interpretability and regulatory alignment as the primary obstacles to institutional integration. The study concludes that AI provides a definitive "early-warning" advantage, shifting currency risk management from reactive mitigation to proactive strategic governance.
Keywords
Artificial Intelligence, Bitcoin, Cointegration, Currency Risk, Deep Learning, Early Warning Systems (EWS), Exchange Rate Volatility, Financial Stability