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Modeling persistence of volatility in the Moroccan exchange market using a fractionally integrated EGARCH

Author

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  • Ouael EL JEBARI

    (University Hassan II of Casablanca, Faculty of law economics and social sciences of Aîn Sebaâ, Morocco.)

  • Abdelati HAKMAOUI

    (University Hassan II of Casablanca, Faculty of law economics and social sciences of Aîn Sebaâ, Morocco.)

Abstract

We have tried in this article to detect, examine, and analyze the persistence in the conditional volatility of the major Moroccan stock market index called MASI, using a fractionally integrated EGARCH model that has the property of capturing long memory along with shocks to the conditional volatility. A GARCH (1,1) and IGARCH models were also estimated for comparative purposes using Akaike, Schwarz and log likelihood information criterion. We used daily returns of MASI index covering the period between 04/01/1993 and 03/02/2017. Our results confirm the presence of a strong persistence in the volatility of the Moroccan index which is inconsistent with the weak efficiency form of Fama’s efficient markets hypothesis. The findings of this study could be of particular use to investors and academics interested in the forecasting of daily volatility in the Moroccan context. This paper broadens previous long memory estimation research by applying a FIEGARCH specification enabling it, not only to account for persistence, but also, to measure the leverage effect. Moreover, we believe that, to the best of our knowledge, this paper is the first to model the volatility of the Moroccan stock market using a FIEGARCH approachng.

Suggested Citation

  • Ouael EL JEBARI & Abdelati HAKMAOUI, 2017. "Modeling persistence of volatility in the Moroccan exchange market using a fractionally integrated EGARCH," Turkish Economic Review, KSP Journals, vol. 4(4), pages 388-399, December.
    • Handle: RePEc:ksp:journ2:v:4:y:2017:i:4:p:388-399
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    References listed on IDEAS

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    1. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    2. Chou, Ray Yeutien, 1988. "Volatility Persistence and Stock Valuations: Some Empirical Evidence Using Garch," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(4), pages 279-294, October-D.
    3. Schwert, G William & Seguin, Paul J, 1990. "Heteroskedasticity in Stock Returns," Journal of Finance, American Finance Association, vol. 45(4), pages 1129-1155, September.
    4. N. Antonakakis & J. Darby, 2013. "Forecasting volatility in developing countries' nominal exchange returns," Applied Financial Economics, Taylor & Francis Journals, vol. 23(21), pages 1675-1691, November.
    5. Epaminondas Panas, 2001. "Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 395-402.
    6. Chow, K Victor & Pan, Ming-Shium & Sakano, Ryoichi, 1996. "On the Long-Term or Short-Term Dependence in Stock Prices: Evidence from International Stock Markets," Review of Quantitative Finance and Accounting, Springer, vol. 6(2), pages 181-194, March.
    7. Qiu, Tian & Chen, Guang & Zhong, Li-Xin & Wu, Xiao-Run, 2012. "Dynamics of bid–ask spread return and volatility of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2656-2666.
    8. Pilar Grau-Carles, 2005. "Tests of Long Memory: A Bootstrap Approach," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 103-113, February.
    9. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    10. Kasman, Adnan & Kasman, Saadet & Torun, Erdost, 2009. "Dual long memory property in returns and volatility: Evidence from the CEE countries' stock markets," Emerging Markets Review, Elsevier, vol. 10(2), pages 122-139, June.
    11. Chambers, Marcus J, 1998. "Long Memory and Aggregation in Macroeconomic Time Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 1053-1072, November.
    12. Hyun J. Jin & Darren L. Frechette, 2004. "Fractional Integration in Agricultural Futures Price Volatilities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 432-443.
    13. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Long memory volatility in Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1425-1433.
    14. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
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    More about this item

    Keywords

    Volatility; Persistence; Long memory; FIEGARCH; MASI.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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