Forecasting and Hedging the Volatility Index of Financial Markets via a Robust XGBoost Model

This study explores predictive modelling of the S&P 500 VIX through machine learning, integrating macroeconomic indicators and market sentiment data to construct a robust XGBoost framework. Empirical results demonstrate the model’s superior capability in capturing non-linear dynamics compared to traditional models (e.g., GARCH, Logit), achieving 94% accuracy in stable markets. Through rigorous validation including rolling-window Value-at-Risk analysis and cumulative return evaluations, we establish a novel volatility forecasting system that provides actionable insights for dynamic risk management during market turbulence. This research contributes an actionable framework for dynamic risk exposure management, offering strategic insights for portfolio managers and policymakers, providing actionable insights for institutional investors and policymakers.