Volatility forecast using hybrid Neural Network models

Werner Kristjanpoller, Anton Fadic, Marcel C. Minutolo

Research output: Contribution to journalArticle

56 Citations (Scopus)

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. © 2013 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)2437-2442
Number of pages6
JournalExpert Systems with Applications
DOIs
Publication statusPublished - 1 Apr 2014

Fingerprint Dive into the research topics of 'Volatility forecast using hybrid Neural Network models'. Together they form a unique fingerprint.

  • Cite this