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Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange |
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| Authors: | Melike Bildirici Özgür Ömer Ersin |
| Affiliation: | 1. Departamento de Industrias, Universidad Técnica Federico Santa María, Chile, Av. España 1680, Valparaíso, Chile;2. Department Management, Robert Morris University, 324 Massey 6001 University Blvd Moon Township, PA 15108, United State;2. Department Management, Robert Morris University, 324 Massey 6001 University Blvd Moon Township, PA 15108, United States\n;1. Business School, Hohai University, China;2. College of Mechanics and Materials, Hohai University, China |
| 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. |
| Keywords: | Volatility Stock returns ARCH/GARCH EGARCH TGARCH PGARCH APGARCH Artificial neural networks |
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