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The performance of bid-ask spread estimators under less than ideal conditions

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  • Michael Bleaney
  • Zhiyong Li

Abstract

The performance of bid-ask spread estimators is investigated using simulation experiments. All estimators are much more accurate if the data are sampled at high frequency. In high-frequency data, the Huang-Stoll estimator, which requires order flow information, generally outperforms Roll-type estimators based on price information only. The exception is when there is feedback trading (order flows respond to past price movements), when the Huang-Stoll estimator is seriously biased. When only low-frequency (e.g. daily) data are available, the Corwin-Schultz estimator based on daily high and low prices is usually less inaccurate than the Huang-Stoll and Roll estimators. An important and empirically relevant exception is when the spread varies within the day; in this case the Corwin-Schultz estimator significantly overestimates the true spread. For a published version, please see Studies in Economics and Finance, Vol. 32 (2015).

Suggested Citation

  • Michael Bleaney & Zhiyong Li, 2013. "The performance of bid-ask spread estimators under less than ideal conditions," Discussion Papers 13/05, University of Nottingham, School of Economics.
    • Handle: RePEc:not:notecp:13/05
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    References listed on IDEAS

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    1. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Hasbrouck, Joel, 2004. "Liquidity in the Futures Pits: Inferring Market Dynamics from Incomplete Data," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(2), pages 305-326, June.
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    Cited by:

    1. Klova, Valeriia & Odegaard, Bernt Arne, 2018. "Equity trading costs have fallen less than commonly thought. Evidence using alternative trading cost estimators," UiS Working Papers in Economics and Finance 2018/4, University of Stavanger, revised 2019.
    2. Chen, Xiaohong & Linton, Oliver & Schneeberger, Stefan & Yi, Yanping, 2019. "Semiparametric estimation of the bid–ask spread in extended roll models," Journal of Econometrics, Elsevier, vol. 208(1), pages 160-178.
    3. Xiaohong Chen & Oliver Linton & Stefan Schneeberger & Yanping Yi, 2016. "Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model," Cowles Foundation Discussion Papers 2033, Cowles Foundation for Research in Economics, Yale University.
    4. Michael Bleaney & Zhiyong Li, 2016. "A new spread estimator," Review of Quantitative Finance and Accounting, Springer, vol. 47(1), pages 179-211, July.
    5. Li, Zhiyong & Lambe, Brendan & Adegbite, Emmanuel, 2018. "New bid-ask spread estimators from daily high and low prices," International Review of Financial Analysis, Elsevier, vol. 60(C), pages 69-86.
    6. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2019. "Liquidity, surprise volume and return premia in the oil market," Energy Economics, Elsevier, vol. 77(C), pages 93-104.
    7. Chen, Xiaohong & Linton, Oliver & Yi, Yanping, 2017. "Semiparametric identification of the bid–ask spread in extended Roll models," Journal of Econometrics, Elsevier, vol. 200(2), pages 312-325.

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    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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