IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2017-45.html
   My bibliography  Save this paper

Measuring Transaction Costs in the Absence of Timestamps

Author

Abstract

This paper develops measures of transaction costs in the absence of transaction timestamps and information about who initiates transactions, which are data limitations that often arise in studies of over-the-counter markets. I propose new measures of the effective spread and study the performance of all estimators analytically, in simulations, and present an empirical illustration with small-cap stocks for the 2005-2014 period. My theoretical, simulation, and empirical results provide new insights into measuring transaction costs and may help guide future empirical work.

Suggested Citation

  • Filip Zikes, 2017. "Measuring Transaction Costs in the Absence of Timestamps," Finance and Economics Discussion Series 2017-045, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2017-45
    DOI: 10.17016/FEDS.2017.045
    as

    Download full text from publisher

    File URL: https://www.federalreserve.gov/econres/feds/files/2017045pap.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.17016/FEDS.2017.045?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
    ---><---

    References listed on IDEAS

    as
    1. Benos, Evangelos & Wetherilt, Anne & Zikes, Filip, 2013. "Financial Stability Paper No 25: The structure and dynamics of the UK CDS market," Bank of England Financial Stability Papers 25, Bank of England.
    2. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    3. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    4. Huang, Roger D & Stoll, Hans R, 1997. "The Components of the Bid-Ask Spread: A General Approach," The Review of Financial Studies, Society for Financial Studies, vol. 10(4), pages 995-1034.
    5. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    6. Yacine Aït-Sahalia & Jean Jacod, 2014. "High-Frequency Financial Econometrics," Economics Books, Princeton University Press, edition 1, number 10261.
    7. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    8. Michael W. Brandt & Francis X. Diebold, 2006. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," The Journal of Business, University of Chicago Press, vol. 79(1), pages 61-74, January.
    9. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    10. Benos, Evangelos & Payne, Richard & Vasios, Michalis, 2020. "Centralized Trading, Transparency, and Interest Rate Swap Market Liquidity: Evidence from the Implementation of the Dodd–Frank Act," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(1), pages 159-192, February.
    11. Madhavan, Ananth & Richardson, Matthew & Roomans, Mark, 1997. "Why Do Security Prices Change? A Transaction-Level Analysis of NYSE Stocks," The Review of Financial Studies, Society for Financial Studies, vol. 10(4), pages 1035-1064.
    12. Paul Schultz, 2001. "Corporate Bond Trading Costs: A Peek Behind the Curtain," Journal of Finance, American Finance Association, vol. 56(2), pages 677-698, April.
    13. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    14. Amy K. Edwards & Lawrence E. Harris & Michael S. Piwowar, 2007. "Corporate Bond Market Transaction Costs and Transparency," Journal of Finance, American Finance Association, vol. 62(3), pages 1421-1451, June.
    15. Foucault, Thierry & Pagano, Marco & Roell, Ailsa, 2013. "Market Liquidity: Theory, Evidence, and Policy," OUP Catalogue, Oxford University Press, number 9780199936243.
    16. Bessembinder, Hendrik & Maxwell, William & Venkataraman, Kumar, 2006. "Market transparency, liquidity externalities, and institutional trading costs in corporate bonds," Journal of Financial Economics, Elsevier, vol. 82(2), pages 251-288, November.
    17. Bessembinder, Hendrik, 2003. "Issues in assessing trade execution costs," Journal of Financial Markets, Elsevier, vol. 6(3), pages 233-257, May.
    18. Jankowitsch, Rainer & Nashikkar, Amrut & Subrahmanyam, Marti G., 2011. "Price dispersion in OTC markets: A new measure of liquidity," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 343-357, February.
    19. Benos, Evangelos & Zikes, Filip, 2016. "Liquidity determinants in the UK gilt market," Bank of England working papers 600, Bank of England.
    20. Liudas Giraitis & George Kapetanios & Anne Wetherilt & Filip ŽIKEŠ, 2016. "Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 58-84, January.
    21. Shane A. Corwin & Paul Schultz, 2012. "A Simple Way to Estimate Bid‐Ask Spreads from Daily High and Low Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 719-760, April.
    22. Michael J. Fleming & John Jackson & Ada Li & Asani Sarkar & Patricia Zobel, 2012. "An analysis of OTC interest rate derivatives transactions: implications for public reporting," Staff Reports 557, Federal Reserve Bank of New York.
    23. 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.
    24. Goyenko, Ruslan Y. & Holden, Craig W. & Trzcinka, Charles A., 2009. "Do liquidity measures measure liquidity?," Journal of Financial Economics, Elsevier, vol. 92(2), pages 153-181, May.
    25. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
    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. Craig W. Holden & Stacey Jacobsen & Avanidhar Subrahmanyam, 2014. "The Empirical Analysis of Liquidity," Foundations and Trends(R) in Finance, now publishers, vol. 8(4), pages 263-365, December.
    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. Joseph, Andreas & Vasios, Michalis & Maizels, Olga & Shreyas, Ujwal & Tanner, John, 2019. "OTC microstructure in a period of stress: a multi‑layered network approach," Bank of England working papers 832, Bank of England.
    4. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    5. Joseph, Andreas & Vasios, Michalis, 2022. "OTC Microstructure in a period of stress: A Multi-layered network approach," Journal of Banking & Finance, Elsevier, vol. 138(C).
    6. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    7. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
    8. 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.
    9. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    10. Benos, Evangelos & Žikeš, Filip, 2018. "Funding constraints and liquidity in two-tiered OTC markets," Journal of Financial Markets, Elsevier, vol. 39(C), pages 24-43.
    11. Yaming Chang, 2025. "Improving volatility forecasts of the Nikkei 225 stock index using a realized EGARCH model with realized and realized range-based volatilities," Papers 2502.02695, arXiv.org, revised Feb 2025.
    12. Díaz, Antonio & Escribano, Ana, 2020. "Measuring the multi-faceted dimension of liquidity in financial markets: A literature review," Research in International Business and Finance, Elsevier, vol. 51(C).
    13. Han, Song & Huang, Alan Guoming & Kalimipalli, Madhu & Wang, Ke, 2022. "Information and liquidity of over-the-counter securities: Evidence from public registration of Rule 144A bonds," Journal of Financial Markets, Elsevier, vol. 59(PB).
    14. Xu, Yanyan & Huang, Dengshi & Ma, Feng & Qiao, Gaoxiu, 2019. "Liquidity and realized range-based volatility forecasting: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1102-1113.
    15. Bannouh, Karim & Martens, Martin & van Dijk, Dick, 2013. "Forecasting volatility with the realized range in the presence of noise and non-trading," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 535-551.
    16. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    17. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
    18. Goldstein, Michael A. & Namin, Elmira Shekari, 2023. "Corporate bond liquidity and yield spreads: A review," Research in International Business and Finance, Elsevier, vol. 65(C).
    19. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
    20. Olga Cielinska & Andreas Joseph & Ujwal Shreyas & John Tanner & Michalis Vasios, 2017. "Gauging market dynamics using trade repository data: The case of the Swiss franc de-pegging," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.

    More about this item

    Keywords

    Effective spread; Simulated method of moments; Time-varying estimation; Transaction costs;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

    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:fip:fedgfe:2017-45. 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: Ryan Wolfslayer ; Keisha Fournillier (email available below). General contact details of provider: https://edirc.repec.org/data/frbgvus.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.