Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange

BİLDİRİCİ M. E., ERSİN Ö. Ö.

Expert Systems with Applications, vol.36, no.4, pp.7355-7362, 2009 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 4
  • Publication Date: 2009
  • Doi Number: 10.1016/j.eswa.2008.09.051
  • Journal Name: Expert Systems with Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.7355-7362
  • Keywords: APGARCH, ARCH/GARCH, Artificial neural networks, EGARCH, PGARCH, Stock returns, TGARCH, Volatility
  • İstanbul Ticaret University Affiliated: No

Abstract

In the study, we discussed the ARCH/GARCH family models and enhanced them with artificial neural networks to evaluate the volatility of daily returns for 23.10.1987-22.02.2008 period in Istanbul Stock Exchange. We proposed ANN-APGARCH model to increase the forecasting performance of APGARCH model. The ANN-extended versions of the obtained GARCH models improved forecast results. It is noteworthy that daily returns in the ISE show strong volatility clustering, asymmetry and nonlinearity characteristics. © 2008 Elsevier Ltd. All rights reserved.