Volatility forecast using hybrid Neural Network models

•An Artificial Neural Network model was used to improve financial forecasts.•Significant reduction in the error of financial forecasts is demonstrated.•Artificial Neural Network models outperformed a simple GARCH(1, 1) in forecasting.•Differences in performance increase varied across Latin American markets.

In this research the testing of a hybrid Neural Networks-GARCH model for volatility forecast is performed in three Latin-American stock exchange indexes from Brazil, Chile and Mexico. A detail of the methodology and application of the volatility forecast of financial series using a hybrid artificial Neural Network model are presented.The results demonstrate that the ANN models can improve the forecasting performance of the GARCH models when studied in the three Latin-American markets and it is shown that the results are robust and consistent for different ANN specifications and different volatility measures.