Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 4, Pages 7355-7362Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.09.051
Keywords
Volatility; Stock returns; ARCH/GARCH; EGARCH; TGARCH; PGARCH; APGARCH; Artificial neural networks
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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. (C) 2008 Elsevier Ltd. All rights reserved.
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