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Volatility dynamics of the Tunisian stock market before and during the COVID‐19 outbreak: Evidence from the GARCH family models

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  • Mohamed Fakhfekh
  • Ahmed Jeribi
  • Marwa Ben Salem

Abstract

The aim of this article is to choose the appropriate GARCH model to analyse the volatility dynamics of the Tunisian sectorial stock market indices during the COVID‐19 outbreak period. We explore the optimal conditional heteroscedasticity model with regards to goodness‐of‐fit to these sectorial indices. In particular, it proposes four models (EGARCH, FIGARCH, FIEGARCH and TGARCH) to measure asymmetric and persistence volatility. Our findings point to three interesting results. First, following the COVID‐19 outbreak, volatility is more persistent in all series. Second, the results show that building constructs materials, construction and food and beverage sector return volatilities have an insignificant asymmetric effect while consumer service, financials and distribution, industrials, basic materials and banks sector return volatilities have relatively high positive and significant asymmetric effect compared with those during the pre‐COVID‐19 period. Finally, the findings show that financial services, automobile and parts, insurance and TUNINDEX20 sectors have insignificant leverage effect. Our results can thus be useful to investors when accounting for future volatility and implementing hedging strategies under COVID‐19 crisis.

Suggested Citation

  • Mohamed Fakhfekh & Ahmed Jeribi & Marwa Ben Salem, 2023. "Volatility dynamics of the Tunisian stock market before and during the COVID‐19 outbreak: Evidence from the GARCH family models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1653-1666, April.
    • Handle: RePEc:wly:ijfiec:v:28:y:2023:i:2:p:1653-1666
    DOI: 10.1002/ijfe.2499
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    References listed on IDEAS

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    Cited by:

    1. Ionuț Nica & Ștefan Ionescu & Camelia Delcea & Nora Chiriță, 2024. "Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms," Risks, MDPI, vol. 12(2), pages 1-42, February.
    2. Huang, Yirong & Luo, Yi, 2024. "Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

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