Forecasting Stock Market Volatility: A Forecast Combination Approach

Rafik Nazarian, Nadiya Gandali Alikhani (), Esmaeil Naderi and Ashkan Amiri

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Stock Return; Long Memory; Neural Network; Hybrid Models. (search for similar items in EconPapers)
JEL-codes: C14 C22 C45 C53 (search for similar items in EconPapers)
Date: 2013-03-15
New Economics Papers: this item is included in nep-cmp, nep-ets, nep-fmk, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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