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What drives the volume-volatility relationship on Euronext Paris?

Author

Listed:
  • Waël Louhichi

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

Abstract

The goal of this paper is to shed light on the relationship between volume and volatility. More specifically, it aims to determine which component of trading volume (trade size or number of transactions) drives this relation. Our intraday analysis reveals several results. Firstly, we confirm the strong positive relationship between volume and volatility. Secondly, including volume in the conditional variance of stock returns significantly reduces the persistence of volatility. Thirdly, we show that the well-known positive relationship between volatility and volume is generated by the number of trades. These results are robust, even after controlling for the impact of the intraday patterns. Finally, our findings are available for the CAC40 Index as well as for individual stocks.

Suggested Citation

  • Waël Louhichi, 2011. "What drives the volume-volatility relationship on Euronext Paris?," Post-Print halshs-00601370, HAL.
    • Handle: RePEc:hal:journl:halshs-00601370
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    Citations

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    Cited by:

    1. Go, You-How & Lau, Wee-Yeap, 2020. "The impact of global financial crisis on informational efficiency: Evidence from price-volume relation in crude palm oil futures market," Journal of Commodity Markets, Elsevier, vol. 17(C).
    2. Karaa, Rabaa & Slim, Skander & Hmaied, Dorra Mezzez, 2018. "Trading intensity and the volume-volatility relationship on the Tunis Stock Exchange," Research in International Business and Finance, Elsevier, vol. 44(C), pages 88-99.
    3. Jawadi, Fredj & Louhichi, Waël & Ameur, Hachmi Ben & Cheffou, Abdoulkarim Idi, 2016. "On oil-US exchange rate volatility relationships: An intraday analysis," Economic Modelling, Elsevier, vol. 59(C), pages 329-334.
    4. Antonakakis, Nikolaos & Floros, Christos & Kizys, Renatas, 2016. "Dynamic spillover effects in futures markets: UK and US evidence," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 406-418.
    5. Naeem, Muhammad & Bouri, Elie & Boako, Gideon & Roubaud, David, 2020. "Tail dependence in the return-volume of leading cryptocurrencies," Finance Research Letters, Elsevier, vol. 36(C).
    6. Zied Ftiti & Fredj Jawadi & Waël Louhichi, 2017. "Modelling the relationship between future energy intraday volatility and trading volume with wavelet," Applied Economics, Taylor & Francis Journals, vol. 49(20), pages 1981-1993, April.
    7. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    8. Thomas Dimpfl & Stefania Odelli, 2020. "Bitcoin Price Risk—A Durations Perspective," JRFM, MDPI, vol. 13(7), pages 1-18, July.
    9. Saif Siddiqui & Preeti Roy, 2019. "Asymmetric relationship between implied volatility, index returns and trading volume: an application of quantile regression model," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 46(3), pages 239-252, September.
    10. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.

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