A hybrid modeling approach for forecasting the volatility of S&P 500 index return

Authors:
E. Hajizadeh;A. Seifi;M. H. Fazel Zarandi;I. B. Turksen
Affiliations:
Department of Industrial Engineering, Amirkabir University of Technology (Polytechnic of Tehran), P.O. Box 15875-4413, Tehran, Iran;Department of Industrial Engineering, Amirkabir University of Technology (Polytechnic of Tehran), P.O. Box 15875-4413, Tehran, Iran;Department of Industrial Engineering, Amirkabir University of Technology (Polytechnic of Tehran), P.O. Box 15875-4413, Tehran, Iran;Department of Mechanical and Industrial Engineering, University of Toronto, 5 King College Road, Toronto, ON, Canada M5S2H8 and Department of Industrial Engineering, TOBB Economy and Technology Un ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Forecasting volatility is an essential step in many financial decision makings. GARCH family of models has been extensively used in finance and economics, particularly for estimating volatility. The motivation of this study is to enhance the ability of GARCH models in forecasting the return volatility. We propose two hybrid models based on EGARCH and Artificial Neural Networks to forecast the volatility of S&P 500 index. The estimates of volatility obtained by an EGARCH model are fed forward to a Neural Network. The input to the first hybrid model is complemented by historical values of other explanatory variables. The second hybrid model takes as inputs both series of the simulated data and explanatory variables. The forecasts obtained by each of those hybrid models have been compared with those of EGARCH model in terms of closeness to the realized volatility. The computational results demonstrate that the second hybrid model provides better volatility forecasts.