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An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?

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  • Manel Hamdi

    () (International Financial Group-Tunisia, Faculty of Economics and Management of Tunis, University of Tunis)

  • Walid Chkili

    () (International Financial Group-Tunisia, Faculty of Economics and Management of Tunis, University of Tunis)

Abstract

The aim of this paper is to study the volatility and forecast accuracy of the Islamic stock market. For this purpose, we construct a new hybrid GARCH-type models based on artificial neural network (ANN). This model is applied to daily prices for DW Islamic markets during the period June 1999-December 2016. Our in-sample results show that volatility of Islamic stock market can be better described by the FIAPARCH approach that take into account asymmetry and long memory features. Considering the out of sample analysis, we have applied a hybrid forecasting model, which combines the FIAPARCH approach and the artificial neural network (ANN). Empirical results show that the proposed hybrid model (FIAPARCH-ANN) outperforms all other single models such as GARCH, FIGARCH, FIAPARCH in terms of all performance criteria used in our study.

Suggested Citation

  • Manel Hamdi & Walid Chkili, 2019. "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?," Working Papers 13, Economic Research Forum, revised 21 Aug 2019.
  • Handle: RePEc:erg:wpaper:13
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    References listed on IDEAS

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