IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v42y2026i2p570-586.html

Volatility forecasting for low-volatility investing

Author

Listed:
  • Conrad, Christian
  • Kleen, Onno
  • Lönn, Rasmus

Abstract

Low-volatility investing often involves sorting and selecting stocks based on retrospective risk measures, for example, the historical standard deviation of returns. In contrast, we employ volatility forecasts from various volatility models to sort, select, and estimate portfolio weights on the 500 largest US stocks. We find that exploiting a large set of time-series models delivers large, significant economic gains compared to traditional benchmarks. After accounting for transaction costs, a low-volatility portfolio based on volatility forecasts from a panel heterogeneous autoregression model and a portfolio based on forecast combinations perform best and can be easily implemented in real time.

Suggested Citation

  • Conrad, Christian & Kleen, Onno & Lönn, Rasmus, 2026. "Volatility forecasting for low-volatility investing," International Journal of Forecasting, Elsevier, vol. 42(2), pages 570-586.
    • Handle: RePEc:eee:intfor:v:42:y:2026:i:2:p:570-586
    DOI: 10.1016/j.ijforecast.2025.08.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207025000743
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2025.08.006?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:intfor:v:42:y:2026:i:2:p:570-586. 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/ijforecast .

    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.