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Volume and Volatility in a Common-Factor Mixture of Distributions Model

Author

Listed:
  • He, Xiaojun
  • Velu, Raja

Abstract

This paper develops a multi-asset mixture distribution hypothesis model to investigate commonality in stock returns and trading volume. The model makes two main predictions: First, the factor structures of returns and trading volume are independent although they stem from the same valuation fundamentals and jointly depend on a latent information flow; second, cross-sectional positive volatility-volume relations arise solely from the dynamic features of the information flow. Empirical analyses at the market level support these predictions. Furthermore, the results indicate that removing the information flow significantly reduces the return volatility persistence and the extent of the reduction exhibits a size pattern.

Suggested Citation

  • He, Xiaojun & Velu, Raja, 2014. "Volume and Volatility in a Common-Factor Mixture of Distributions Model," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(1), pages 33-49, February.
    • Handle: RePEc:cup:jfinqa:v:49:y:2014:i:01:p:33-49_00
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    Citations

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    Cited by:

    1. Wang, Jianxin, 2022. "Market distraction and near-zero daily volatility persistence," International Review of Financial Analysis, Elsevier, vol. 80(C).
    2. Fredj Jawadi & Waël Louhichi & Abdoulkarim Idi Cheffou & Rivo Randrianarivony, 2016. "Intraday jumps and trading volume: a nonlinear Tobit specification," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1167-1186, November.
    3. repec:uts:finphd:39 is not listed on IDEAS
    4. 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.
    5. Fei Su & Lei Wang, 2020. "Conditional Volatility Persistence and Realized Volatility Asymmetry: Evidence from the Chinese Stock Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(14), pages 3252-3269, November.
    6. Wang, Zijun & Qian, Yan & Wang, Shiwen, 2018. "Dynamic trading volume and stock return relation: Does it hold out of sample?," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 195-210.
    7. Ran Xiao, 2019. "Essays on Price Discovery and Volatility Dynamics in Emerging Market Currencies," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 5-2019.
    8. Ames, Matthew & Bagnarosa, Guillaume & Peters, Gareth W., 2017. "Violations of uncovered interest rate parity and international exchange rate dependences," Journal of International Money and Finance, Elsevier, vol. 73(PA), pages 162-187.
    9. Chou, Ke-Hsin & Day, Min-Yuh & Chiu, Chien-Liang, 2023. "Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 365-385.
    10. repec:uts:finphd:38 is not listed on IDEAS
    11. Fei Su, 2018. "Essays on Price Discovery and Volatility Dynamics in the Foreign Exchange Market," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2018.
    12. Zhou, Xinquan & Bagnarosa, Guillaume & Gohin, Alexandre & Pennings, Joost M.E. & Debie, Philippe, 2023. "Microstructure and high-frequency price discovery in the soybean complex," Journal of Commodity Markets, Elsevier, vol. 30(C).

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