IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v5y1999i2p109-122.html
   My bibliography  Save this article

Estimation of the effective bid-ask spread on high frequency Danish bond data

Author

Listed:
  • K. Nyholm

Abstract

In this paper the effective bid-ask spread is estimated using 12 high frequency Danish bond samples. A clear-cut MA(1)-model for the mean of the return series, and a GARCH(1,1)-model for the variance, are found. Basically, Roll's model is used, but three different methods of calculating the first-order autocovariance are suggested. Each of these in turn produces three possible ways of estimating the effective bid-ask spread. First, Roll's original autocovariance estimate is used. Second, the autocovariance is calculating using the parameters of an estimated MA(1) model. Third, the autocovariance is obtained from the parameters of a joint MA(1)-GARCH(1,1) model. By means of bootstrapping the standard error of the bid-ask spread estimates are found. It is shown that the gain in efficiency, measured by the relative difference in the standard error of the estimates, is 29% when going from method one to method two, but only 1% when going from method two to method three. These results indicate that the extra gain in efficiency obtained by taking account of the MA(1) structure of the data is noteworthy, but the gain when incorporating the GARCH-effects is negligible.

Suggested Citation

  • K. Nyholm, 1999. "Estimation of the effective bid-ask spread on high frequency Danish bond data," The European Journal of Finance, Taylor & Francis Journals, vol. 5(2), pages 109-122.
    • Handle: RePEc:taf:eurjfi:v:5:y:1999:i:2:p:109-122
    DOI: 10.1080/135184799337127
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/135184799337127
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/135184799337127?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Choi, J. Y. & Salandro, Dan & Shastri, Kuldeep, 1988. "On the Estimation of Bid-Ask Spreads: Theory and Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(2), pages 219-230, June.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. George, Thomas J & Kaul, Gautam & Nimalendran, M, 1991. "Estimation of the Bid-Ask Spread and Its Components: A New Approach," The Review of Financial Studies, Society for Financial Studies, vol. 4(4), pages 623-656.
    4. James L. Hamilton, 1991. "The Dealer And Market Concepts Of Bid-Ask Spread: A Comparison For Nasdaq Stocks," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 14(2), pages 129-139, June.
    5. Ken Nyholm, 1997. "Estimation of the bid/ask spread on Danish stocks, an evaluation of Roll's estimator," Applied Financial Economics, Taylor & Francis Journals, vol. 7(6), pages 605-610.
    6. Hamilton, James L, 1991. "The Dealer and Market Concepts of Bid-Ask Spread: A Comparison for NASDAQ Stocks," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 14(2), pages 129-139, Summer.
    7. Roll, Richard, 1984. "A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market," Journal of Finance, American Finance Association, vol. 39(4), pages 1127-1139, September.
    8. Harris, Lawrence, 1990. "Statistical Properties of the Roll Serial Covariance Bid/Ask Spread Estimator," Journal of Finance, American Finance Association, vol. 45(2), pages 579-590, June.
    Full references (including those not matched with items on IDEAS)

    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. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    2. Alexandros Gabrielsen & Massimiliano Marzo & Paolo Zagaglia, 2011. "Measuring market liquidity: An introductory survey," Papers 1112.6169, arXiv.org.
    3. Stelios Katranidis & Theodore Panagiotidis & Costas Zontanos, 2014. "An Evaluation Of The Greek Universities’ Economics Departments," Bulletin of Economic Research, Wiley Blackwell, vol. 66(2), pages 173-182, April.
    4. 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.
    5. Andres, Christian & Cumming, Douglas & Karabiber, Timur & Schweizer, Denis, 2014. "Do markets anticipate capital structure decisions? — Feedback effects in equity liquidity," Journal of Corporate Finance, Elsevier, vol. 27(C), pages 133-156.
    6. Ahn, Hee-Joon & Cai, Jun & Hamao, Yasushi & Ho, Richard Y.K., 2005. "Adverse selection, brokerage coverage, and trading activity on the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 29(6), pages 1483-1508, June.
    7. Ahn, Hee-Joon & Cai, Jun & Hamao, Yasushi & Ho, Richard Y. K., 2002. "The components of the bid-ask spread in a limit-order market: evidence from the Tokyo Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 9(4), pages 399-430, November.
    8. George Tannous & Juan Wang & Craig Wilson, 2013. "The Intraday Pattern of Information Asymmetry, Spread, and Depth: Evidence from the NYSE," International Review of Finance, International Review of Finance Ltd., vol. 13(2), pages 215-240, June.
    9. G. Wuyts, 2007. "Stock Market Liquidity.Determinants and Implications," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(2), pages 279-316.
    10. Martin Angerer & Georg Peter & Sebastian Stoeckl & Thomas Wachter & Matthias Bank & Marco Menichetti, 2018. "Bid-Ask Spread Patterns and the Optimal Timing for Discretionary Liquidity Traders on Xetra," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 70(3), pages 209-230, July.
    11. Dionigi Gerace & Qigui Liu & Gary Gang Tian & Willa Zheng, 2015. "Call Auction Transparency and Market Liquidity: Evidence from China," International Review of Finance, International Review of Finance Ltd., vol. 15(2), pages 223-255, June.
    12. Gao, Yang & Li, Yunhai & Wang, Yaojun & Wang, Chao & Liu, Chao, 2019. "Asymptotic comparison of three spread estimators based on Roll’s model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 420-432.
    13. Lin, Chien-Chih, 2014. "Estimation accuracy of high–low spread estimator," Finance Research Letters, Elsevier, vol. 11(1), pages 54-62.
    14. Pascual, Roberto & Escribano, Álvaro & Tapia, Mikel, 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.
    15. 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.
    16. Michael Bleaney & Zhiyong Li, 2016. "A new spread estimator," Review of Quantitative Finance and Accounting, Springer, vol. 47(1), pages 179-211, July.
    17. David Abad & Antonio Rubia, 2005. "Modelos De Estimacion De La Probabilidad De Negociacion Informada: Una Comparacion Metodologica En El Mercado Español," Working Papers. Serie EC 2005-12, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    18. Louis R. Mercorelli & David Michayluk & Anthony D. Hall, 2008. "Modelling Adverse Selection on Electronic Order-Driven Markets," Research Paper Series 220, Quantitative Finance Research Centre, University of Technology, Sydney.
    19. David Abad & Antonio Rubia, 2004. "Estimating The Probability Of Informed Trading: Further Evidence From An Order-Driven Market," Working Papers. Serie AD 2004-38, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    20. Vitale, Paolo, 1998. "Two months in the life of several gilt-edged market makers on the London Stock Exchange," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 8(3-4), pages 299-324, December.

    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:taf:eurjfi:v:5:y:1999:i:2:p:109-122. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/REJF20 .

    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.