AI-Driven Currency Arbitrage Optimization System and Correlation Risk Using Statistical Analysis

The increasing complexity of global financial markets and their need for advanced techniques for currency arbitrage and risk assessment. This research delivers an AI-based currency arbitrage optimizer which implements XGBoost and LSTM models for USD/INR and USD/JPY and JPY/INR currency markets prediction. The system utilizes Bellman-Ford, which is a Garch(1,1) model to find profitable currency arbitrage opportunities from forecasted values while the Bellman-Ford algorithm creates trade routes using linear programming and risk evaluation relies on Sharpe Ratio and Monte Carlo Simulation. The evaluation of volatility spillover effects between two oil-based economies relies on statistical techniques which include GARCH modeling for performing correlation risk analysis. The evaluation of our experimental approach performed using historical forex data from twenty years revealed enhanced accuracy in forecasts while generating better risk-adjusted returns.