Volatility forecast using hybrid Neural Network models

Authors:
Werner Kristjanpoller;Anton Fadic;Marcel C. Minutolo
Affiliations:
Departamento de Industrias, Universidad Técnica Federico Santa María, Chile, Av. España 1680, Valparaíso, Chile;Departamento de Industrias, Universidad Técnica Federico Santa María, Chile, Av. España 1680, Valparaíso, Chile;Department Management, Robert Morris University, 324 Massey 6001 University Blvd Moon Township, PA 15108, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

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Abstract

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.