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There's more to volatility than volume

Author

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  • Laszlo Gillemot
  • J. Doyne Farmer
  • Fabrizio Lillo
Abstract
It is widely believed that fluctuations in transaction volume, as reflected in the number of transactions and to a lesser extent their size, are the main cause of clustered volatility. Under this view bursts of rapid or slow price diffusion reflect bursts of frequent or less frequent trading, which cause both clustered volatility and heavy tails in price returns. We investigate this hypothesis using tick by tick data from the New York and London Stock Exchanges and show that only a small fraction of volatility fluctuations are explained in this manner. Clustered volatility is still very strong even if price changes are recorded on intervals in which the total transaction volume or number of transactions is held constant. In addition the distribution of price returns conditioned on volume or transaction frequency being held constant is similar to that in real time, making it clear that neither of these are the principal cause of heavy tails in price returns. We analyse recent results of Ane and Geman (2000: J. Finance, 55, 2259-2284) and Gabaix et al. (2003: Nature, 423, 267-270), and discuss the reasons why their conclusions differ from ours. Based on a cross-sectional analysis we show that the long-memory of volatility is dominated by factors other than transaction frequency or total trading volume.

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  • Laszlo Gillemot & J. Doyne Farmer & Fabrizio Lillo, 2006. "There's more to volatility than volume," Quantitative Finance, Taylor & Francis Journals, vol. 6(5), pages 371-384.
    • Handle: RePEc:taf:quantf:v:6:y:2006:i:5:p:371-384
    DOI: 10.1080/14697680600835688
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    2. Mike, Szabolcs & Farmer, J. Doyne, 2008. "An empirical behavioral model of liquidity and volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 200-234, January.
    3. Matthieu Wyart & Jean-Philippe Bouchaud & Julien Kockelkoren & Marc Potters & Michele Vettorazzo, 2006. "Relation between Bid-Ask Spread, Impact and Volatility in Double Auction Markets," Science & Finance (CFM) working paper archive 500067, Science & Finance, Capital Fund Management.
    4. Timoth'ee Fabre & Vincent Ragel, 2023. "Interpretable ML for High-Frequency Execution," Papers 2307.04863, arXiv.org, revised Sep 2024.
    5. J. Doyne Farmer & Austin Gerig & Fabrizio Lillo & Henri Waelbroeck, 2013. "How efficiency shapes market impact," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1743-1758, November.
    6. Damien Challet & Nikita Gourianov, 2018. "Dynamical regularities of US equities opening and closing auctions," Post-Print hal-01702726, HAL.
    7. Ata Türkoğlu, 2016. "Normally distributed high-frequency returns: a subordination approach," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 389-409, March.
    8. Zheng, Zeyu & Gui, Jun & Qiao, Zhi & Fu, Yang & Stanley, H.Eugene & Li, Baowen, 2019. "New dynamics between volume and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1343-1350.
    9. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    10. Matthieu Wyart & Jean-Philippe Bouchaud & Julien Kockelkoren & Marc Potters & Michele Vettorazzo, 2008. "Relation between bid-ask spread, impact and volatility in order-driven markets," Quantitative Finance, Taylor & Francis Journals, vol. 8(1), pages 41-57.
    11. Andersen, Torben G. & Bollerslev, Tim & Dobrev, Dobrislav, 2007. "No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications," Journal of Econometrics, Elsevier, vol. 138(1), pages 125-180, May.
    12. Torben G. Andersen & Tim Bollerslev & Per Frederiksen & Morten Ørregaard Nielsen, 2010. "Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 233-261.
    13. Rafael Velasco-Fuentes & Wing Lon Ng, 2011. "Nonlinearities in stochastic clocks: trades and volume as subordinators of electronic markets," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 863-881.
    14. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    15. Yamamoto, Ryuichi, 2011. "Order aggressiveness, pre-trade transparency, and long memory in an order-driven market," Journal of Economic Dynamics and Control, Elsevier, vol. 35(11), pages 1938-1963.
    16. Z. Sun & P. A. Hamill & Y. Li & Y. C. Yang & S. A. Vigne, 2019. "Did long-memory of liquidity signal the European sovereign debt crisis?," Annals of Operations Research, Springer, vol. 282(1), pages 355-377, November.
    17. Marcus Cordi & Serge Kassibrakis & Damien Challet, 2018. "The market nanostructure origin of asset price time reversal asymmetry," Working Papers hal-01966419, HAL.
    18. Szabolcs Mike & J. Doyne Farmer, 2005. "An empirical behavioral model of price formation," Papers physics/0509194, arXiv.org, revised Oct 2005.
    19. Marcus Cordi & Damien Challet & Serge Kassibrakis, 2021. "The market nanostructure origin of asset price time reversal asymmetry," Quantitative Finance, Taylor & Francis Journals, vol. 21(2), pages 295-304, February.
    20. Gianbiagio Curato & Fabrizio Lillo, 2013. "Modeling the coupled return-spread high frequency dynamics of large tick assets," Papers 1310.4539, arXiv.org.
    21. Jean-Philippe Bouchaud, 2024. "The Self-Organized Criticality Paradigm in Economics & Finance," Papers 2407.10284, arXiv.org, revised Sep 2024.
    22. Mingjie Ji & Honggang Li, 2016. "Exploring Price Fluctuations in a Double Auction Market," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 189-209, August.

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