Volatility Modeling Using GARCH and Machine Learning Hybrids

Abstract

Financial decisions heavily rely on volatility forecasting to prevent risks and determine derivative values as well as control portfolio distributions and automated trading systems. Financial time series contain inherent complex nonlinear patterns that remain elusive to the linear standard tools of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model along with its variants (EGARCH, GJR-GARCH and so on). The authors develop and test hybrid models which unite GARCH-based modeling techniques with Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (XGBoost) as well as Long Short-Term Memory (LSTM) neural networks.
This analysis examines a wide range of financial instruments through daily return data from the S&P 500, FTSE 100, EUR/USD, USD/JPY and Bitcoin, Ethereum trading pairs. The volatility estimation starts through GARCH and then the process continues by incorporating ML models into ensemble learning or residual correction to improve forecast accuracy. The evaluation method calculates performance based on statistical error metrics (MAE, RMSE, MAPE) in combination with directional accuracy measurements and Diebold-Mariano testing which determines model predictive accuracy.
The experimental data demonstrates that GARCH-ML combination models provide superior outcomes when compared to both GARCH and ML models specifically during high-volatility situations along with market disruptions. XGBoost-based hybrids with LSTM demonstrate outstanding performance in temporal effects and circuit changes and power shifts. Additional tests verify that these linked forecasting systems function consistently with diverse financial instruments along with varied time projection amounts.
The research enhances knowledge regarding intelligent financial modeling by presenting detailed findings about hybrid volatility forecasting approaches. The output proves that using machine learning approaches alongside interpretable statistical tools creates superior financial analytics tools since they retain both methods' benefits.