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Market Liquidity and Flow-driven Risk

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  • Prachi Deuskar
  • Timothy C. Johnson

Abstract

Using a unique dataset of trades and limit orders for S&P 500 futures, we decompose the aggregate risk into a component driven by the impact of net market orders and a component unrelated to net orders. The first component--flow-driven risk--is large, accounting for approximately 50% of market variance, and it is not transient. This risk represents the joint effect of net trade demand and the price impact of that demand--i.e., illiquidity. We find that flows are largely unpredictable, and lagged flows have no price impact. Flow-driven risk is time varying because price impact is highly variable. Illiquidity rises with market volatility, but not with flow uncertainty. Net selling increases illiquidity, which amplifies downside flow-driven risk. The findings are consistent with flow-driven shocks resulting from fluctuations in aggregate risk-bearing capacity. Under this interpretation, investors with constant risk tolerance should trade against such shocks (i.e., "supply liquidity") to achieve substantial utility gains. Quantitatively accounting for the scale of flow-driven risk poses a major challenge for asset pricing theory. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Suggested Citation

  • Prachi Deuskar & Timothy C. Johnson, 2011. "Market Liquidity and Flow-driven Risk," The Review of Financial Studies, Society for Financial Studies, vol. 24(3), pages 721-753.
    • Handle: RePEc:oup:rfinst:v:24:y:2011:i:3:p:721-753
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    File URL: http://hdl.handle.net/10.1093/rfs/hhq132
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    Cited by:

    1. Craig W. Holden & Stacey Jacobsen & Avanidhar Subrahmanyam, 2014. "The Empirical Analysis of Liquidity," Foundations and Trends(R) in Finance, now publishers, vol. 8(4), pages 263-365, December.
    2. Marshall, Ben R. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2013. "ETF arbitrage: Intraday evidence," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3486-3498.
    3. Chung, Dennis Y. & Hrazdil, Karel, 2012. "Speed of convergence to market efficiency: The role of ECNs," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 702-720.
    4. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," Journal of Financial Economics, Elsevier, vol. 135(2), pages 293-319.
    5. Opschoor, Anne & Taylor, Nick & van der Wel, Michel & van Dijk, Dick, 2014. "Order flow and volatility: An empirical investigation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 185-201.
    6. Dang, Viet Anh & Michayluk, David & Pham, Thu Phuong, 2018. "The curious case of changes in trading dynamics: When firms switch from NYSE to NASDAQ," Journal of Financial Markets, Elsevier, vol. 41(C), pages 17-35.
    7. Algieri, Bernardina, 2012. "Price Volatility, Speculation and Excessive Speculation in Commodity Markets: sheep or shepherd behaviour?," Discussion Papers 124390, University of Bonn, Center for Development Research (ZEF).
    8. Cenesizoglu, Tolga & Grass, Gunnar, 2018. "Bid- and ask-side liquidity in the NYSE limit order book," Journal of Financial Markets, Elsevier, vol. 38(C), pages 14-38.
    9. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," LSE Research Online Documents on Economics 100409, London School of Economics and Political Science, LSE Library.
    10. Jean-Philippe Bouchaud, 2021. "The Inelastic Market Hypothesis: A Microstructural Interpretation," Papers 2108.00242, arXiv.org, revised Jan 2022.
    11. Algieri, Bernardina & Leccadito, Arturo, 2019. "Price volatility and speculative activities in futures commodity markets: A combination of combinations of p-values test," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 40-54.
    12. Bassem Salhi & Saad Alflayyeh, 2016. "Impact of Speculation and Bubble Detection in Stock Markets: The Tunisian and the Moroccan Cases," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 7(2), pages 73-89, May.
    13. Lee, Chien-Chiang & Wang, Chih-Wei & Ho, Shan-Ju, 2022. "Financial aid and financial inclusion: Does risk uncertainty matter?," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    14. Johnson, Timothy C., 2016. "Rethinking reversals," Journal of Financial Economics, Elsevier, vol. 120(2), pages 211-228.
    15. Brunetti, Celso & Büyükşahin, Bahattin & Harris, Jeffrey H., 2016. "Speculators, Prices, and Market Volatility," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(5), pages 1545-1574, October.
    16. Deuskar, Prachi & Johnson, Timothy C., 2021. "Funding liquidity and market liquidity in government bonds," Journal of Banking & Finance, Elsevier, vol. 129(C).
    17. Zhang, Hao, 2018. "Intraday patterns in foreign exchange returns and realized volatility," Finance Research Letters, Elsevier, vol. 27(C), pages 99-104.
    18. Wu, Liang & Liu, Hengzhi & Liu, Chang & Long, Yunshen, 2020. "Determining the information share of liquidity and order flows in extreme price movements," Economic Modelling, Elsevier, vol. 93(C), pages 559-575.
    19. Siikanen, Milla & Kanniainen, Juho & Luoma, Arto, 2017. "What drives the sensitivity of limit order books to company announcement arrivals?," Economics Letters, Elsevier, vol. 159(C), pages 65-68.
    20. Siikanen, Milla & Kanniainen, Juho & Valli, Jaakko, 2017. "Limit order books and liquidity around scheduled and non-scheduled announcements: Empirical evidence from NASDAQ Nordic," Finance Research Letters, Elsevier, vol. 21(C), pages 264-271.

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