Forecasting Financial Volatility Under Structural Breaks: A Comparative Study of GARCH Models and Deep Learning Techniques

The main objective of this study is to evaluate the predictive performance of traditional econometric models and deep learning techniques in forecasting financial volatility under structural breaks. Using daily data from four Latin American stock market indices between 2000 and 2024, we compare GARCH models with neural networks such as LSTM and CNN. Structural breaks are identified through a modified ICSS algorithm and incorporated into the GARCH framework via regime segmentation. The results show that neglecting breaks overstates volatility persistence and weakens predictive accuracy, while accounting for them improves GARCH forecasts only in specific cases. By contrast, deep learning models consistently outperform GARCH alternatives at medium- and long-term horizons, capturing nonlinear and time-varying dynamics more effectively. This study contributes to the literature by bridging econometric and deep learning approaches and offers practical insights for policymakers and investors in emerging markets facing recurrent structural instability.