Nazarian, Rafik and Gandali Alikhani, Nadiya and Naderi, Esmaeil and Amiri, Ashkan (2013): Forecasting Stock Market Volatility: A Forecast Combination Approach.

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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 ARFIMAFIGARCH model, the accuracy of the hybrid model of neural network and best GARCH was higher than each one of these models.
Item Type:  MPRA Paper 

Original Title:  Forecasting Stock Market Volatility: A Forecast Combination Approach 
English Title:  Forecasting Stock Market Volatility: A Forecast Combination Approach 
Language:  English 
Keywords:  Stock Return, Long Memory, Neural Network, Hybrid Models. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods 
Item ID:  46786 
Depositing User:  esmeil naderi 
Date Deposited:  07. May 2013 05:35 
Last Modified:  07. May 2013 06:05 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/46786 