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.