IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v24y2018i10p809-827.html
   My bibliography  Save this article

Estimation of log-GARCH models in the presence of zero returns

Author

Listed:
  • Genaro Sucarrat
  • Alvaro Escribano

Abstract

A critique that has been directed towards the log-GARCH model is that its log-volatility specification does not exist in the presence of zero returns. A common ‘remedy’ is to replace the zeros with a small (in the absolute sense) non-zero value. However, this renders estimation asymptotically biased if the true return is equal to zero with probability zero. Here, we propose a solution. If the zero probability is zero, then zero returns may be observed because of non-trading, measurement error (e.g. due to rounding), missing values and other data issues. The algorithm we propose treats the zeros as missing values and handles these by estimation via the ARMA representation. An extensive number of simulations verify the conjectured properties of the bias-correcting algorithm, and several empirical applications illustrate that it can make a substantial difference in practice.

Suggested Citation

  • Genaro Sucarrat & Alvaro Escribano, 2018. "Estimation of log-GARCH models in the presence of zero returns," The European Journal of Finance, Taylor & Francis Journals, vol. 24(10), pages 809-827, July.
    • Handle: RePEc:taf:eurjfi:v:24:y:2018:i:10:p:809-827
    DOI: 10.1080/1351847X.2017.1336452
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1351847X.2017.1336452
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1351847X.2017.1336452?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.

    Citations

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


    Cited by:

    1. Andrey Shternshis & Piero Mazzarisi & Stefano Marmi, 2022. "Efficiency of the Moscow Stock Exchange before 2022," Papers 2207.10476, arXiv.org, revised Jul 2022.
    2. Philipp Otto, 2022. "A Multivariate Spatial and Spatiotemporal ARCH Model," Papers 2204.12472, arXiv.org.
    3. Sucarrat, Genaro, 2018. "The Log-GARCH Model via ARMA Representations," MPRA Paper 100386, University Library of Munich, Germany.
    4. Christian M. Hafner & Dimitra Kyriakopoulou, 2021. "Exponential-Type GARCH Models With Linear-in-Variance Risk Premium," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 589-603, March.
    5. Pourkhanali, Armin & Tafakori, Laleh & Bee, Marco, 2023. "Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Yuanhua Feng & Thomas Gries & Sebastian Letmathe, 2023. "FIEGARCH, modulus asymmetric FILog-GARCH and trend-stationary dual long memory time series," Working Papers CIE 156, Paderborn University, CIE Center for International Economics.
    7. Bonnier, Jean-Baptiste, 2022. "Forecasting crude oil volatility with exogenous predictors: As good as it GETS?," Energy Economics, Elsevier, vol. 111(C).

    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:taf:eurjfi:v:24:y:2018:i:10:p:809-827. 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: 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.