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Measuring the Liquidity Part of Volume

Author

Listed:
  • Serge Darolles

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Gaëlle Le Fol

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Gulten Mero
Abstract
Based on the concept that the presence of liquidity frictions can increase the daily traded volume, we develop an extended version of the mixture of distribution hypothesis model (MDH) along the lines of Tauchen and Pitts (1983) to measure the liquidity portion of volume. Our approach relies on a structural definition of liquidity frictions arising from the theoretical framework of Grossman and Miller (1988), which explains how liquidity shocks affect the way in which information is incorporated into daily trading characteristics. In addition, we propose an econometric setup exploiting the volatility–volume relationship to filter the liquidity portion of volume and infer the presence of liquidity frictions using daily data. Finally, based on FTSE 100 stocks, we show that the extended MDH model proposed here outperforms that of Andersen (1996) and that the liquidity frictions are priced in the cross-section of stock returns.

Suggested Citation

  • Serge Darolles & Gaëlle Le Fol & Gulten Mero, 2015. "Measuring the Liquidity Part of Volume," Post-Print hal-01632766, HAL.
    • Handle: RePEc:hal:journl:hal-01632766
    DOI: 10.1016/j.jbankfin.2014.09.007
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    References listed on IDEAS

    as
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    Cited by:

    1. Antonio A. F. Santos, 2021. "Bayesian Estimation for High-Frequency Volatility Models in a Time Deformed Framework," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 455-479, February.
    2. Maria Ludovica Drudi & Giulio Carlo Venturi, 2023. "Assessing the liquidity premium in the Italian bond market," Questioni di Economia e Finanza (Occasional Papers) 795, Bank of Italy, Economic Research and International Relations Area.
    3. Gulten Mero & Serge Darolles & Gaëlle Le Fol, 2015. "Financial Market Liquidity: Who Is Acting Strategically?," THEMA Working Papers 2015-14, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    4. 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.
    5. Ying Jiang & Neil Kellard & Xiaoquan Liu, 2020. "Night trading and market quality: Evidence from Chinese and US precious metal futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(10), pages 1486-1507, October.
    6. Darolles, Serge & Le Fol, Gaëlle & Mero, Gulten, 2017. "Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows," Journal of Econometrics, Elsevier, vol. 201(2), pages 367-383.
    7. Ranaldo, Angelo & de Magistris, Paolo Santucci, 2022. "Liquidity in the global currency market," Journal of Financial Economics, Elsevier, vol. 146(3), pages 859-883.
    8. Angelo Ranaldo & Paolo Santucci de Magistris, 2018. "Trading Volume, Illiquidity and Commonalities in FX Markets," Working Papers on Finance 1823, University of St. Gallen, School of Finance, revised Oct 2019.
    9. Eduardo Bered Fernandes Vieira & Tiago Pascoal Filomena, 2020. "Liquidity Constraints for Portfolio Selection Based on Financial Volume," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 1055-1077, December.
    10. Xu, Liao & Gao, Han & Shi, Yukun & Zhao, Yang, 2020. "The heterogeneous volume-volatility relations in the exchange-traded fund market: Evidence from China," Economic Modelling, Elsevier, vol. 85(C), pages 400-408.
    11. Priyanka Naik & Y. V. Reddy, 2021. "Stock Market Liquidity: A Literature Review," SAGE Open, , vol. 11(1), pages 21582440209, January.
    12. 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.
    13. Staer, Arsenio & Sottile, Pedro, 2018. "Equivalent volume and comovement," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 143-157.
    14. Li, Jie & Ren, Da & Feng, Xu & Zhang, Yongjie, 2016. "Network of listed companies based on common shareholders and the prediction of market volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 508-521.
    15. Liu, Bin & Xia, XiangYang & Xiao, Wen, 2020. "Public information content and market information efficiency: A comparison between China and the U.S," China Economic Review, Elsevier, vol. 60(C).
    16. Francisco Javier Vasquez-Tejos & Prosper Lamothe Fernández, 2020. "Liquidity Risk and Stock Return in Latin American Emerging Markets," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 20(1), pages 57-74.

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    More about this item

    Keywords

    Volatility-volume relationship; Mixture of distribution hypothesis; Information-based trading; Liquidity shocks; GMM tests; Liquidity arbitrage;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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