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The Application of GARCH and EGARCH in Modeling the Volatility of Daily Stock Returns During Massive Shocks: The Empirical Case of Egypt

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  • Ezzat, Hassan

Abstract

Modeling volatility during a financial crisis where massive shocks are generated presents an ideal environment for investigating the dynamics of volatility during periods of extreme fluctuations for comparison with volatility during more tranquil periods. The objective of this paper is to study volatility of daily stock returns listed on the Egyptian Exchange during the political turmoil of 2011. The analysis is based on employing both GARCH and EGARCH models. Daily closing prices of four Egyptian stock market indices, the EGX 30, EGX70, EGX 100, and the EGX 20 capped were used in the analysis. The time frame was from the inception of each index to the 30th of June 2012. The sample period covers the period of pre-and post the Egyptian revolution which was shaped by extreme volatile fluctuations in stock returns. The EGARCH model was the method of choice for modeling the volatility in order to investigate the long memory and the leverage effect in the volatilities of the two periods. The findings reveal higher volatility during the revolution period for all indices reflected in higher standard deviations for both daily returns and absolute returns, with the EGX 70 displaying the highest volatility. The leverage effect was more apparent during the revolution period. However, long memory was more apparent during the pre-revolution period.

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  • Ezzat, Hassan, 2012. "The Application of GARCH and EGARCH in Modeling the Volatility of Daily Stock Returns During Massive Shocks: The Empirical Case of Egypt," MPRA Paper 50530, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:50530
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    Cited by:

    1. Ibrahim, Omar, 2019. "Modelling Risk on the Egyptian Stock Market: Evidence from a Markov-Regime Switching GARCH Process," MPRA Paper 98091, University Library of Munich, Germany.
    2. Curtis Nybo, 2021. "Sector Volatility Prediction Performance Using GARCH Models and Artificial Neural Networks," Papers 2110.09489, arXiv.org.
    3. Roni Bhowmik & Wu Chao & Wang Shouyang & Jewel Roy Kumar, 2017. "A Study on the Volatility of the Bangladesh Stock Market — Based on GARCH Type Models," Journal of Systems Science and Information, De Gruyter, vol. 5(3), pages 193-215, June.
    4. Ezzat, Hassan, 2012. "The Application of GARCH Methods in Modeling Volatility Using Sector Indices from the Egyptian Exchange," MPRA Paper 51584, University Library of Munich, Germany.
    5. Charilaos Mertzanis & Noha Allam, 2018. "Political Instability and Herding Behaviour: Evidence from Egypt’s Stock Market," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(1), pages 29-59, April.
    6. Charan Raj Chimrani & Farhan Ahmed & Vinesh Kumar Panjwani, 2018. "Modeling Sectoral Stock Indexes Volatility: Empirical Evidence from Pakistan Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 8(2), pages 319-324.

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    More about this item

    Keywords

    The Egyptian Exchange; ARCH; GARCH; EGARCH; Volatility; Revolution;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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