Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6092
Title: A hybrid modeling approach for forecasting the volatility of S&P 500 index return
Authors: Hajizadeh, E.
Seifi, A.
Zarandi, M. N. Fazel
Türkşen, İsmail Burhan
Keywords: Volatility
GARCH models
Simulated series
Artificial Neural Networks
Realized volatility
Issue Date: 2012
Publisher: Pergamon-Elsevier Science Ltd
Source: 1st International Symposium on Computing in Science and Engineering -- JUN 03-05, 2010 -- Kusadasi, TURKEY
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. (C) 2011 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2011.07.033
https://hdl.handle.net/20.500.11851/6092
ISSN: 0957-4174
1873-6793
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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