IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v88y2026ics0927539826000575.html

Option-implied variance asymmetry and stock market returns

Author

Listed:
  • Zhang, Yuanyi
  • Ma, Jiantao

Abstract

This paper introduces an option-based, market-level signal derived from individual stocks’ Implied Variance Asymmetry (IVA) using the Three-Pass Regression Filter (3PRF), which we refer to as IVA-3PRF. We find that IVA-3PRF is significantly positively related to future stock market returns: a one–standard deviation increase in IVA-3PRF predicts a 0.88% increase in market returns in the following month. Out-of-sample forecasts based on IVA-3PRF achieve an R2 of 3.79% (5.34%) using a minimum training window of five (ten) years. We show that the predictive power of IVA-3PRF arises from its ability to forecast future changes in economic conditions and, consequently, future cash-flow news. Importantly, the signal is most informative during pessimistic periods, when option investors’ expectations appear more rational. This conditional rationality is driven by heightened investor attention to macroeconomic conditions in such times. By contrast, in optimistic periods, elevated sentiment induces overconfidence and biased expectations, reducing the signal’s predictive power.

Suggested Citation

  • Zhang, Yuanyi & Ma, Jiantao, 2026. "Option-implied variance asymmetry and stock market returns," Journal of Empirical Finance, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:empfin:v:88:y:2026:i:c:s0927539826000575
    DOI: 10.1016/j.jempfin.2026.101743
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539826000575
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jempfin.2026.101743?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eee:empfin:v:88:y:2026:i:c:s0927539826000575. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jempfin .

    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.