IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v82y2025ics0927539825000428.html
   My bibliography  Save this article

The role of macro-finance factors in predicting stock market volatility: A latent threshold dynamic model

Author

Listed:
  • Maheu, John M.
  • Shamsi Zamenjani, Azam

Abstract

Measuring, modeling, and forecasting volatility are of great importance in financial applications such as asset pricing, portfolio management, and risk management. In this paper, we investigate predictability of stock market volatility by macro-finance variables in a dynamic regression framework using latent thresholding. The latent threshold models allow data-driven shrinkage of regression coefficients by collapsing them to zero for irrelevant predictor variables and allowing for time-varying nonzero coefficients when supported by the data. This is a parsimonious framework which selects what potential predictor variables should be included in the regressions and when. We extend this model to allow for stochastic volatility for realized volatility innovations and discuss Bayesian estimation methods. We apply the models to monthly S&P 500 and NASDAQ 100 volatility and find that using macro-finance variables in volatility forecasts enhances model performance statistically and economically, particularly when we allow for dynamic inclusion/exclusion of these variables.

Suggested Citation

  • Maheu, John M. & Shamsi Zamenjani, Azam, 2025. "The role of macro-finance factors in predicting stock market volatility: A latent threshold dynamic model," Journal of Empirical Finance, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:empfin:v:82:y:2025:i:c:s0927539825000428
    DOI: 10.1016/j.jempfin.2025.101620
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539825000428
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jempfin.2025.101620?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.

    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:82:y:2025:i:c:s0927539825000428. 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.