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Market Liquidity and Flow-driven Risk

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  • Prachi Deuskar
  • Timothy C. Johnson
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    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.

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    File URL: http://hdl.handle.net/10.1093/rfs/hhq132
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    Bibliographic Info

    Article provided by Society for Financial Studies in its journal Review of Financial Studies.

    Volume (Year): 24 (2011)
    Issue (Month): 3 ()
    Pages: 721-753

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    Handle: RePEc:oup:rfinst:v:24:y:2011:i:3:p:721-753

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    Cited by:
    1. Marshall, Ben R. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2013. "ETF arbitrage: Intraday evidence," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3486-3498.
    2. 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.
    3. 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).

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