IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v35y2022i8p3710-3741..html
   My bibliography  Save this article

Subjective Bond Returns and Belief Aggregation

Author

Listed:
  • Andrea Buraschi
  • Ilaria Piatti
  • Paul Whelan

Abstract

This paper proposes an aggregation scheme of subjective bond return expectations based on the historical accuracy of professional interest rate forecasters. We use disaggregated survey data on bond returns and document large disagreement in the cross-sectional distribution and persistence in forecast accuracy. Our aggregate subjective belief proxy outperforms equal weighting schemes, and its dynamics are significantly different from statistical forecasting models. With this measure in hand, we study the relationship between quantities of risk and subjective expectations of excess returns and demonstrate a strong link between the two, even if such a relationship is difficult to detect using realized returns.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Andrea Buraschi & Ilaria Piatti & Paul Whelan, 2022. "Subjective Bond Returns and Belief Aggregation," The Review of Financial Studies, Society for Financial Studies, vol. 35(8), pages 3710-3741.
    • Handle: RePEc:oup:rfinst:v:35:y:2022:i:8:p:3710-3741.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhab115
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nagel, Stefan & Xu, Zhengyang, 2023. "Dynamics of subjective risk premia," Journal of Financial Economics, Elsevier, vol. 150(2).
    2. Albert S. Kyle & Anna A. Obizhaeva & Yajun Wang, 2023. "Beliefs Aggregation and Return Predictability," Journal of Finance, American Finance Association, vol. 78(1), pages 427-486, February.

    More about this item

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • G4 - Financial Economics - - Behavioral Finance
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:oup:rfinst:v:35:y:2022:i:8:p:3710-3741.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.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.