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

Authors:
Melike Bildirici;Özgür Ömer Ersin
Affiliations:
Yıldız Technical University, Faculty of Economics and Business Administration, Department of Economics, Barbaros Bulvari, Besiktas, İstanbul, Turkey;Yeditepe University, Faculty of Commerce, Department of International Trade and Business, Kadikoy, Kayisdagi, İstanbul, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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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.