IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v36y2023i10p4190-4232..html
   My bibliography  Save this article

When Do Low-Frequency Measures Really Measure Effective Spreads? Evidence from Equity and Foreign Exchange Markets

Author

Listed:
  • Mohammad R
  • Filip Zikes

Abstract

We present evidence that several popular low-frequency measures of effective spread suffer from a volatility-induced bias and that volatility is the primary driver of the variation of these liquidity proxies. Using data for U.S. equities and major foreign exchange rates, we show that the bias arises when the effective spread is small relative to volatility. We document that the bias has become more acute over time and show that volatility-biased measures fail to replicate some well-known results in empirical finance. We conclude by providing guidance on the choice of low-frequency measures in empirical applications.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Mohammad R & Filip Zikes, 2023. "When Do Low-Frequency Measures Really Measure Effective Spreads? Evidence from Equity and Foreign Exchange Markets," The Review of Financial Studies, Society for Financial Studies, vol. 36(10), pages 4190-4232.
    • Handle: RePEc:oup:rfinst:v:36:y:2023:i:10:p:4190-4232.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhad028
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    More about this item

    Keywords

    C15; C58; G12; G20; F31;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • F31 - International Economics - - International Finance - - - Foreign Exchange

    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:oup:rfinst:v:36:y:2023:i:10:p:4190-4232.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.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.