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The Relationship Between Stock Market Volatility And Trading Volume: Evidence From South Africa

Author

Listed:
  • Pramod Kumar Naik
  • Rangan Gupta
  • Puja Padhi

    (Central University of Rajasthan, India
    University of Pretoria, South Africa
    Indian Institute of Technology Bombay, India)

Abstract

The rate of information flow into the market in generating market volatility has been a much debated issue in the finance research. Moreover, since the rate of information cannot be directly observable and quantifiable, trading volume has been considered to be a good proxy for incorporating such information. The mixture of distribution hypothesis (MDH) and the sequential information hypothesis (SIAH) provide the theoretical justification for the relationship between trading volume and market volatility. This paper revisits the relationship between equity trading volume and returns volatility for the Johannesburg Stock Exchange (JSE) of South Africa using daily data over the period of 6th July 2006 to 31st August 2016. Further, it analyzed an after-crisis period, i.e., 1/04/2008 to 8/31/2016, in order to verify the findings. EGARCH and Granger causality models are employed to analyze the volume-volatility relationship. Also the level of volatility persistence has been compared before and after the inclusion of trading volume in the volatility model as an exogenous variable. The analysis shows that the JSE exhibits volatility asymmetry implying that the return volatility responds more to the bad news than to the good news. The relationship between trading volume and market volatility is found to be positive and contemporaneous supporting the MDH. This study also uncover that the volatility persistence remains high even after the inclusion of trading volume as an explanatory variable in the volatility model. The above set of results also holds for the post-crisis sub-sample. Furthermore, the pairwise Granger causality tests indicate a feedback relationship between volume and volatility only in the case of the sub-sample. But for the full sample, a unidirectional causality between volume and volatility, with trading volume Granger causes market volatility was observed. As it was observed that there is a positive and contemporaneous relationship between volatility, and also that the trading volumes cannot attenuate the level of volatility persistence, findings advise the equity market participants to be cautious in their trading behavior; and through messages to the regulators that risk management practice should be strengthened in order to control for market volatility that associated with increasing trading volume.

Suggested Citation

  • Pramod Kumar Naik & Rangan Gupta & Puja Padhi, 2018. "The Relationship Between Stock Market Volatility And Trading Volume: Evidence From South Africa," Journal of Developing Areas, Tennessee State University, College of Business, vol. 52(1), pages 99-114, January-M.
    • Handle: RePEc:jda:journl:vol.52:year:2018:issue1:pp:99-114
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    Cited by:

    1. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    2. Ngene, Geoffrey M. & Mungai, Ann Nduati, 2022. "Stock returns, trading volume, and volatility: The case of African stock markets," International Review of Financial Analysis, Elsevier, vol. 82(C).
    3. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment," Mathematics, MDPI, vol. 11(6), pages 1-26, March.
    4. Alessio Emanuele Biondo, 2019. "Order book modeling and financial stability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 469-489, September.
    5. Jaber Yasmina, 2020. "Transactions Volume, Exchange Direction and Asymmetry of Volatility in Emerging Market: Evidence From Tunisian Stock Exchange," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(6), pages 318-336, December.
    6. Elie Bouri & Riza Demirer & Rangan Gupta & Xiaojin Sun, 2020. "The predictability of stock market volatility in emerging economies: Relative roles of local, regional, and global business cycles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 957-965, September.
    7. Mesias Alfeus & Justin Harvey & Phuthehang Maphatsoe, 2025. "Improving realised volatility forecast for emerging markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 49(1), pages 299-342, March.
    8. Afees A. Salisu & Rangan Gupta, 2021. "Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals," Working Papers 202144, University of Pretoria, Department of Economics.
    9. Malay K. Dey & Chaoyan Wang, 2022. "Asymmetric volume volatility causality in dual listing H-shares," Journal of Asset Management, Palgrave Macmillan, vol. 23(5), pages 419-428, September.
    10. Lorraine Muguto & Paul-Francois Muzindutsi, 2022. "A Comparative Analysis of the Nature of Stock Return Volatility in BRICS and G7 Markets," JRFM, MDPI, vol. 15(2), pages 1-27, February.
    11. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," Post-Print hal-04790290, HAL.
    12. Talla M Aldeehani, 2019. "Have Stock Markets Become Less Volatile After the Great Recession?," Research in World Economy, Research in World Economy, Sciedu Press, vol. 10(3), pages 10-25, December.
    13. Jean-Pierre Gueyie & Mouhamadou Saliou Diallo & Mamadou Fadel Diallo, 2022. "Relationship between Stock Returns and Trading Volume at the Bourse Régionale des Valeurs Mobilières, West Africa," IJFS, MDPI, vol. 10(4), pages 1-16, December.
    14. Karima Saci, 2022. "Modelling the Relationship Between Trading Volume and Stock Returns Volatility for Islamic and Conventional Banks: The Case of Saudi Arabia نمذجة العلاقة بين حجم التداول وتقلب عوائد الأسهم للبنوك الإسلامية والتقليدية: حالة المملكة العربية السعودية," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 35(1), pages 41-55, January.
    15. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
    16. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.
    17. Lamine Diane & Pradeep Brijlal, 2024. "Forecasting Stock Market Realized Volatility using Random Forest and Artificial Neural Network in South Africa," International Journal of Economics and Financial Issues, Econjournals, vol. 14(2), pages 5-14, March.
    18. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-40, April.
    19. Kejin Wu & Sayar Karmakar & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa," Working Papers 202326, University of Pretoria, Department of Economics.
    20. repec:bcp:journl:v:9:y:2025:i:11:p:2104-2129 is not listed on IDEAS

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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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