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Forecasting Stock Market Volatility: A Forecast Combination Approach

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  • Nazarian, Rafik
  • Gandali Alikhani, Nadiya
  • Naderi, Esmaeil
  • Amiri, Ashkan

Abstract

Recently, with the development of financial markets and due to the importance of these markets and their close relationship with other macroeconomic variables, using advanced mathematical models with complicated structures for forecasting these markets has become very popular. Besides, neural network models have gained a special position compared to other advanced models due to their high accuracy in forecasting different variables. Therefore, the main purpose of this study was to forecast the volatilities of TSE index by regressive models with long memory feature, feed forward neural network and hybrid models (based on forecast combination approach) using daily data. The results were indicative of the fact that based on the criteria for assessing forecasting error, i.e., MSE and RMSE, although forecasting errors of the feed forward neural network model were less than ARFIMA-FIGARCH model, the accuracy of the hybrid model of neural network and best GARCH was higher than each one of these models.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 46786.

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Date of creation: 15 Mar 2013
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Handle: RePEc:pra:mprapa:46786

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Keywords: Stock Return; Long Memory; Neural Network; Hybrid Models.;

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  1. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  2. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  3. Kittiakarasakun, Jullavut & Tse, Yiuman, 2011. "Modeling the fat tails in Asian stock markets," International Review of Economics & Finance, Elsevier, Elsevier, vol. 20(3), pages 430-440, June.
  4. Delavari, Majid & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," MPRA Paper 45977, University Library of Munich, Germany.
  5. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, Elsevier, vol. 18(4), pages 154-163, September.
  6. Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45615, University Library of Munich, Germany.
  7. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 74(1), pages 3-30, September.
  8. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, Elsevier, vol. 73(1), pages 185-215, July.
  9. Wang, Ju-Jie & Wang, Jian-Zhou & Zhang, Zhe-George & Guo, Shu-Po, 2012. "Stock index forecasting based on a hybrid model," Omega, Elsevier, Elsevier, vol. 40(6), pages 758-766.
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