IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v32y2025i7p951-959.html
   My bibliography  Save this article

A test for change points under the roughness of stochastic volatility: the case of the VIX index

Author

Listed:
  • Qinwen Zhu
  • Xundi Diao
  • Chongfeng Wu

Abstract

In light of the recent empirical studies with high-frequency data, the logarithm of the stock market volatility data behaves as a fractional Brownian motion (fBm) with the Hurst exponent smaller than 0.5. It thus leads to extensive research in the so-called rough volatility (RV). This paper introduces a novel non-parametric test for its change points detection problem, which combines the proposed autoregressive rough volatility (ARRV) model and an increment ratio (IR)-based filtering function. The empirical results of the VIX index show that our method can detect more accurate change points without dependence on initial conditions/parameters, work efficiently for trends and forecast robustly better.

Suggested Citation

  • Qinwen Zhu & Xundi Diao & Chongfeng Wu, 2025. "A test for change points under the roughness of stochastic volatility: the case of the VIX index," Applied Economics Letters, Taylor & Francis Journals, vol. 32(7), pages 951-959, April.
    • Handle: RePEc:taf:apeclt:v:32:y:2025:i:7:p:951-959
    DOI: 10.1080/13504851.2023.2294020
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2023.2294020
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2023.2294020?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:taf:apeclt:v:32:y:2025:i:7:p:951-959. 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/RAEL20 .

    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.