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An examination of the complementary volume–volatility information theories

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  • Zhiyao Chen
  • Robert T. Daigler

Abstract

The volume–volatility relationship during the dissemination stages of information flow is examined by analyzing various theories relating volume and volatility as complementary rather than competing models. The mixture of distributions hypothesis, sequential arrival of information hypothesis, the dispersion of beliefs hypothesis, and the noise trader hypothesis all add to the understanding of how volume and volatility interact for different types of futures traders. An integrated picture of the volume–volatility relationship is provided by investigating the dynamic linear and nonlinear associations between volatility and the volume of informed (institutional) and uninformed (the general public) traders. In particular, the trading behavior explanation for the persistence of futures volatility, the effect of the timing of private information arrival, and the response of institutional traders to excess noise trading risk is examined. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:963–992, 2008

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  • Zhiyao Chen & Robert T. Daigler, 2008. "An examination of the complementary volume–volatility information theories," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(10), pages 963-992, October.
    • Handle: RePEc:wly:jfutmk:v:28:y:2008:i:10:p:963-992
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    Cited by:

    1. Imran Riaz MALIK* & Attaullah SHAH*, 2014. "Market Varying Conditional Risk-Return Relationship," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 24(2), pages 121-142.
    2. Liu, Xinghua & Liu, Xin & Liang, Xiaobei, 2015. "Information-driven trade and price–volume relationship in artificial stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 73-80.
    3. Sila Alan, Nazli & Karagozoglu, Ahmet K. & Korkmaz, Sibel, 2016. "Growing pains: The evolution of new stock index futures in emerging markets," Research in International Business and Finance, Elsevier, vol. 37(C), pages 1-16.
    4. Wen-Cheng Lu & Fang-Jun Lin, 2010. "An Empirical Study Of Volatility And Trading Volume Dynamics Using High-Frequency Data," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 4(3), pages 93-101.
    5. Pham, Son Duy & Nguyen, Thao Thac Thanh & Do, Hung Xuan & Vo, Xuan Vinh, 2023. "Portfolio diversification during the COVID-19 pandemic: Do vaccinations matter?," Journal of Financial Stability, Elsevier, vol. 65(C).
    6. Darolles, Serge & Fol, Gaëlle Le & Mero, Gulten, 2015. "Measuring the liquidity part of volume," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 92-105.
    7. 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.
    8. Slim, Skander & Dahmene, Meriam, 2016. "Asymmetric information, volatility components and the volume–volatility relationship for the CAC40 stocks," Global Finance Journal, Elsevier, vol. 29(C), pages 70-84.
    9. Chuang, Wen-I & Liu, Hsiang-Hsi & Susmel, Rauli, 2012. "The bivariate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility," Global Finance Journal, Elsevier, vol. 23(1), pages 1-15.
    10. Sensoy, Ahmet & Serdengeçti, Süleyman, 2019. "Intraday volume-volatility nexus in the FX markets: Evidence from an emerging market," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 1-12.
    11. Chi Ming Ho, 2013. "Private information, overconfidence and intraday trading behaviour: empirical study of the Taiwan stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 23(4), pages 325-345, February.
    12. Jullavut Kittiakaraskun & Yiuman Tse & George H.K. Wang, 2011. "The Impact of Trading Activity by Trader Types on Asymmetric Volatility in Nasdaq-100 Index Futures," Working Papers 0021, College of Business, University of Texas at San Antonio.
    13. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti & Aris Kartsaklas, 2021. "Investors' trading behaviour and stock market volatility during crisis periods: A dual long‐memory model for the Korean Stock Exchange," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4441-4461, July.
    14. Aris Kartsaklas, 2018. "Trader Type Effects On The Volatility‐Volume Relationship Evidence From The Kospi 200 Index Futures Market," Bulletin of Economic Research, Wiley Blackwell, vol. 70(3), pages 226-250, July.
    15. 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.
    16. Peltomäki, Jarkko, 2017. "Beta as a determinant of investor activity in sector exchange-traded funds," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 137-145.
    17. Todorova, Neda & Clements, Adam E., 2018. "The volatility-volume relationship in the LME futures market for industrial metals," Resources Policy, Elsevier, vol. 58(C), pages 111-124.
    18. Julien Chevallier & Benoît Sévi, 2011. "On the volatility-volume relationship in energy futures markets using intraday data," Working Papers hal-04140997, HAL.
    19. Bhaumik, S. & Karanasos, M. & Kartsaklas, A., 2016. "The informative role of trading volume in an expanding spot and futures market," Journal of Multinational Financial Management, Elsevier, vol. 35(C), pages 24-40.

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