IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v106y2024i4p1114-1128.html
   My bibliography  Save this article

Reading the Candlesticks: An OK Estimator for Volatility

Author

Listed:
  • Jia Li

    (Singapore Management University)

  • Dishen Wang

    (Derivatives China Capital)

  • Qiushi Zhang

    (University of International Business and Economics)

Abstract

We propose an Optimal candlesticK (OK) estimator for the spot volatility using high-frequency candlestick observations. Under a standard infill asymptotic setting, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. Its estimation error can be coupled by a Brownian functional, which permits valid inference. Our theoretical and numerical results suggest that the proposed candlestick-based estimator is much more accurate than the conventional spot volatility estimator based on high-frequency returns. An empirical illustration documents the intraday volatility dynamics of various assets during the Fed chairman’s recent congressional testimony.

Suggested Citation

  • Jia Li & Dishen Wang & Qiushi Zhang, 2024. "Reading the Candlesticks: An OK Estimator for Volatility," The Review of Economics and Statistics, MIT Press, vol. 106(4), pages 1114-1128, July.
  • Handle: RePEc:tpr:restat:v:106:y:2024:i:4:p:1114-1128
    DOI: 10.1162/rest_a_01203
    as

    Download full text from publisher

    File URL: https://doi.org/10.1162/rest_a_01203
    Download Restriction: Access to PDF is restricted to subscribers.

    File URL: https://libkey.io/10.1162/rest_a_01203?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.

    More about this item

    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:tpr:restat:v:106:y:2024:i:4:p:1114-1128. 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: The MIT Press (email available below). General contact details of provider: https://direct.mit.edu/journals .

    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.