IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v70y2024i2p931-951.html
   My bibliography  Save this article

Predicting Bond Return Predictability

Author

Listed:
  • Daniel Borup

    (Department of Economics and Business Economics, Aarhus University, 8210 Aarhus V, Denmark)

  • Jonas N. Eriksen

    (Department of Economics and Business Economics, Aarhus University, 8210 Aarhus V, Denmark; Danish Finance Institute, 2000 Frederiksberg, Denmark)

  • Mads M. Kjær

    (Department of Economics and Business Economics, Aarhus University, 8210 Aarhus V, Denmark; Danish Finance Institute, 2000 Frederiksberg, Denmark)

  • Martin Thyrsgaard

    (InCommodities A/S, 8200 Aarhus N, Denmark)

Abstract

This paper provides empirical evidence on predictable time variations in out-of-sample bond return predictability. Bond return predictability is associated with periods of high (low) economic activity (uncertainty), which implies that violations of the expectations hypothesis are state dependent and linked to features of the business cycle. These state dependencies in predictability, established by introducing a new multivariate test for equal conditional predictive ability, can be used in real time to improve out-of-sample bond risk premia estimates and investors’ economic utility through a novel dynamic forecast combination scheme that uses predicted forecasting performance to identify the best set of methods to include in the combined forecast. Dynamically combined forecasts exhibit strong countercyclical behavior and peak during recessions.

Suggested Citation

  • Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:2:p:931-951
    DOI: 10.1287/mnsc.2023.4713
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2023.4713
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2023.4713?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
    ---><---

    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:inm:ormnsc:v:70:y:2024:i:2:p:931-951. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.