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Evaluated the Success of Fractionally Integrated-GARCH Models on Prediction Stock Market Return Volatility in Gulf Arab Stock Markets

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  • Heitham Al-Hajieh

Abstract

This paper evaluated the different Fractionally Integrated-GARCH Models (FIGARCH BBM's, FIGARCH Chung, FIEGARCH, FIAPARCH BBM's, FIAPARCH Chung, and HYGARCH). This is the first research to use six different Fractionally Integrated-GARCH Models. Most research compares one of Fractionally Integrated-GARCH Models with the traditional GARCH, EGARCH, GJG-GARCH, IGARCH, and APGARCH. To do so, daily returns of Gulf Cooperation Council (GCC) Stock Markets analyzed, covering the period 1995 to 2015. Both the Superior Predictive Ability and the Model Confidence Set tests were used to identify the best fitting models of each country. The results reveal that FIGARCH BBM is the best fitting model for UAE, KSA, and Bahrain. FIEGARCH is the best fitting model for Kuwait. FIGARCH Chung is the best fitting model for Qatar. Only the results for Oman were mixed between FIGARCH BBM and FIAPARCH BBM models.

Suggested Citation

  • Heitham Al-Hajieh, 2017. "Evaluated the Success of Fractionally Integrated-GARCH Models on Prediction Stock Market Return Volatility in Gulf Arab Stock Markets," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(7), pages 200-213, July.
    • Handle: RePEc:ibn:ijefaa:v:9:y:2017:i:7:p:200-213
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    References listed on IDEAS

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    1. Dilip Kumar & S. Maheswaran, 2013. "Asymmetric long memory volatility in the PIIGS economies," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 12(1), pages 23-43, February.
    2. Li, Muyi & Li, Wai Keung & Li, Guodong, 2015. "A new hyperbolic GARCH model," Journal of Econometrics, Elsevier, vol. 189(2), pages 428-436.
    3. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    4. Li, Muyi & Li, Guodong & Li, Wai Keung, 2011. "Score Tests for Hyperbolic GARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 579-586.
    5. Muyi Li & Guodong Li & Wai Keung Li, 2011. "Score Tests for Hyperbolic GARCH Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(4), pages 579-586, October.
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    Cited by:

    1. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    2. Galip Gençyürk, 2024. "Volatility Modeling and Spillover: The Turkish and Russian Stock Markets," Istanbul Business Research, Istanbul University Business School, vol. 53(1), pages 81-101, April.

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    More about this item

    Keywords

    fractionally Integrated-GARCH; forecasting; evaluating; Gulf Cooperation Council (GCC); mean squared error; superior predictive ability;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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