Modelling and analysis the volatility of Dow Jones Islamic Indices Returns Using ARCH Models
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https://doi.org/10.34118/jeemr.v6i1.3586
Résumé
The purpose of this study is modeling and analysis the volatility of Dow Jones Islamic indices though an application of both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic models and daily data of the Dow Jones Islamic Market index returns during the study period. The results show that Dow Jones Islamic Market index returns have the same commonly observed stylized facts of financial time series. Moreover, the best model for volatility modeling is the PGARCH model.
Références
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- Chiadmi, M. (2015). La volatilité des indices boursiers islamiques dans le contexte de la crise financière (Doctoral dissertation). Ecole Mohammadia d'Ingénieurs.
- EL Khamlichi, A. (2012). Éthique et performance: le cas des indices boursiers et des fonds d’investissement en finance islamique (Doctoral dissertation). Ferrand I: Université d'Auvergne-Clermont
- Majidi, E. (2016). Finance islamique et croissance économique: quelles interactions dans les pays MENA (Doctoral dissertation). Université de Pau.
- Al- Ahmad, Z., & Kusai Salman, A. (2019),Modeling Volatility in Emerging Stock Markets: The Case of Damascus Securities Exchange, Tishreen University Journal for Research and Scientific Studies -Economic and Legal Sciences Series, 41(2).
- Ben Nasr, A., & Ajmi, A. (2014),Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model, Applied Financial Economics, 24(14).
- Besseba, A., & Benchiha, K. (2019),Analysis the Effect of Exchange Rate Volatility on Islamic Stock Indices Performance: An Empirical Study of the Dow Jones Islamic Market, Dirassat Journal Economic Issue, 11(1).
- Bollerslev, T. (1986), Generalized autoregressive conditional heteroskedasticity, Journal of econometrics,31(3).
- Chiadmi, M., & Ghaiti , F. (2014),Modeling volatility of Islamic stock indexes: Empirical evidence and comparative analysis, DLSU Business & Economics Review, 24(1).
- EL Khamlichi, A., & Viallefont, A. (2015), La performance des indices boursiers en finance islamique: Une Meta-analyse, Revue de Gestion et d’Économie, 3(1).
- Matei, M. (2009),Assessing volatility forecasting models: why GARCH models take the lead, Romanian Journal of Economic Forecasting, 12(4).
- Namugaya, J., Weke, P., & Charles, W. (2014),modelling stock returns volatility on Uganda securities exchange, Applied Mathematical Sciences, 8(104).
- Nelson, D. (1991),Conditional heteroskedasticity in asset returns: A new approach, Econometrica: Journal of the Econometric Society, pp. 347-370.
- Rejeb, A., & Arfaoui, M. (2019),Do Islamic stock indexes outperform conventional stock indexes? A state space modeling approach, European Journal of Management and Business Economics, 28(3).
- Sahnoune, S., & Benlaib, B. (2019),Volatility Modeling of Islamic Stock Indices Returns Using GARCH Models, El-Bahith Review, 19(1).
- Soualili , S., & Belghait , B. (2018),Modeling the daily return volatility of the CAC 40 index by applying the APGARCH model, Revue des Réformes Economiques et Intégration En Economie Mondiale, 12(26).
- Wiphatthanananthakul, C., & Sriboonchitta, S. (2010),The Comparison among ARMA-GARCH,-EGARCH,-GJR, and-PGARCH models on Thailand Volatility Index, The Thailand Econometrics Society, 2(2).
Publiée
2022-06-05
Comment citer
BOUHAFS, I. (2022). Modelling and analysis the volatility of Dow Jones Islamic Indices Returns Using ARCH Models . Journal of Excellence for Economics and Management Research, 6(1), 409-424. https://doi.org/10.34118/jeemr.v6i1.3586
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Original Article
