IDEAS home Printed from https://ideas.repec.org/p/not/notecp/13-05.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    3. Ho, Thomas & Stoll, Hans R., 1981. "Optimal dealer pricing under transactions and return uncertainty," Journal of Financial Economics, Elsevier, vol. 9(1), pages 47-73, March.
    4. repec:bla:jfinan:v:44:y:1989:i:1:p:115-34 is not listed on IDEAS
    5. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    6. Holden, Craig W., 2009. "New low-frequency spread measures," Journal of Financial Markets, Elsevier, vol. 12(4), pages 778-813, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Bleaney & Zhiyong Li, 2016. "A new spread estimator," Review of Quantitative Finance and Accounting, Springer, vol. 47(1), pages 179-211, July.
    2. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.
    3. Biais, Bruno & Glosten, Larry & Spatt, Chester, 2005. "Market microstructure: A survey of microfoundations, empirical results, and policy implications," Journal of Financial Markets, Elsevier, vol. 8(2), pages 217-264, May.
    4. Corò, Filippo & Dufour, Alfonso & Varotto, Simone, 2013. "Credit and liquidity components of corporate CDS spreads," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5511-5525.
    5. Daniella Acker & Mathew Stalker & Ian Tonks, 2002. "Daily Closing Inside Spreads and Trading Volumes Around Earnings Announcements," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 29(9‐10), pages 1149-1179.
    6. Gabor Pinter & Chaojun Wang & Junyuan Zou, 2024. "Size Discount and Size Penalty: Trading Costs in Bond Markets," The Review of Financial Studies, Society for Financial Studies, vol. 37(7), pages 2156-2190.
    7. Richard K. Lyons, 2002. "Foreign exchange: macro puzzles, micro tools," Economic Review, Federal Reserve Bank of San Francisco, pages 51-69.
    8. Zeynep Cobandag Guloglu & Cumhur Ekinci, 2022. "Liquidity measurement: A comparative review of the literature with a focus on high frequency," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 41-74, February.
    9. Koopman, S.J.M. & Lai, H.N., 1998. "Modelling bid-ask spreads in competitive dealership markets," Other publications TiSEM 7a193911-dbf2-4831-ac8d-9, Tilburg University, School of Economics and Management.
    10. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    11. Kang, Wenjin & Zhang, Huiping, 2014. "Measuring liquidity in emerging markets," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 49-71.
    12. Flannery, Mark J. & Kwan, Simon H. & Nimalendran, M., 2004. "Market evidence on the opaqueness of banking firms' assets," Journal of Financial Economics, Elsevier, vol. 71(3), pages 419-460, March.
    13. Osler, Carol L. & Mende, Alexander & Menkhoff, Lukas, 2011. "Price discovery in currency markets," Journal of International Money and Finance, Elsevier, vol. 30(8), pages 1696-1718.
    14. Thomas Johann & Erik Theissen, 2013. "Liquidity measures," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 10, pages 238-255, Edward Elgar Publishing.
    15. Pascual, Roberto, 2000. "Adverse selection costs, trading activity and liquidity in the NYSE: an empirical analysis in a dynamic context," UC3M Working papers. Economics 7276, Universidad Carlos III de Madrid. Departamento de Economía.
    16. 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.
    17. Jiang, Christine X. & Kim, Jang-Chul & Wood, Robert A., 2002. "The change in trading activity on volatility and adverse selection component: evidence from ADR splits," Journal of Multinational Financial Management, Elsevier, vol. 12(4-5), pages 323-345.
    18. Jagjeev Dosanjh, 2017. "Exchange Initiatives and Market Efficiency: Evidence from the Australian Securities Exchange," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2017, January-A.
    19. Cheng, Louis & Firth, Michael & Leung, T.Y. & Rui, Oliver, 2006. "The effects of insider trading on liquidity," Pacific-Basin Finance Journal, Elsevier, vol. 14(5), pages 467-483, November.
    20. Medina, Vicente & Pardo, Ángel & Pascual, Roberto, 2014. "The timeline of trading frictions in the European carbon market," Energy Economics, Elsevier, vol. 42(C), pages 378-394.

    More about this item

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:not:notecp:13/05. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/denotuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.