An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?
International Journal of Islamic and Middle Eastern Finance and Management
ISSN: 1753-8394
Article publication date: 12 May 2021
Issue publication date: 4 November 2021
Abstract
Purpose
The purpose of this study is to investigate the volatility and forecast accuracy of the Islamic stock market for the period 1999–2017. This period is characterized by the occurrence of several economic and political events such as the September 11, 2001, terrorist attack and the 2007–2008 global financial crisis.
Design/methodology/approach
This study constructs a new hybrid generalized autoregressive conditional heteroskedasticity (GARCH)-type model based on an artificial neural network (ANN). This model is applied to the daily Dow Jones Islamic Market World Index during the period June 1999–January 2017.
Findings
The in-sample results show that the volatility of the Islamic stock market can be better described by the fractionally integrated asymmetric power ARCH (FIAPARCH) approach that takes into account asymmetry and long memory features. Considering the out-of-sample analysis, this paper has applied a hybrid forecasting model, which combines the FIAPARCH approach and the ANN. Empirical results reveal that the proposed hybrid model (FIAPARCH-ANN) outperforms all other single models such as GARCH, fractional integrated GARCH and FIAPARCH in terms of all performance criteria used in the study.
Practical implications
The results have some implications for Islamic investors, portfolio managers and policymakers. These implications are related to the optimal portfolio diversification decision, the hedging strategy choice and the risk management analysis.
Originality/value
The paper develops a new framework that combines an ANN and FIAPARCH model that introduces two important features of time series, namely, asymmetry and long memory.
Keywords
Acknowledgements
The authors are grateful to the editor and anonymous referees for providing useful comments that improved the quality of this paper.
This work was sponsored by the Economic Research Forum (ERF) and has benefited from both financial and intellectual support. The contents and recommendations do not necessarily reflect ERF’s views.
Citation
Chkili, W. and Hamdi, M. (2021), "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?", International Journal of Islamic and Middle Eastern Finance and Management, Vol. 14 No. 5, pp. 853-873. https://doi.org/10.1108/IMEFM-05-2019-0204
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited