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Identification Robust Testing of Risk Premia in Finite Samples

Author

Listed:
  • Frank Kleibergen
  • Lingwei Kong
  • Zhaoguo Zhan

Abstract

The reliability of tests on the risk premia in linear factor models is threatened by limited sample sizes and weak identification of risk premia frequently encountered in applied work. We, therefore, propose novel tests on the risk premia that are robust to both limited sample sizes and the identification strength of the risk premia as reflected by the quality of the risk factors. These tests are appealing for empirically relevant settings, and lead to confidence sets of risk premia that can substantially differ from conventional ones. To show the latter, we revisit two high-profile empirical applications.

Suggested Citation

  • Frank Kleibergen & Lingwei Kong & Zhaoguo Zhan, 2023. "Identification Robust Testing of Risk Premia in Finite Samples," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 263-297.
    • Handle: RePEc:oup:jfinec:v:21:y:2023:i:2:p:263-297.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbac010
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    References listed on IDEAS

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    Cited by:

    1. Mark J. Kamstra & Ruoyao Shi, 2024. "Testing and Ranking of Asset Pricing Models Using the GRS Statistic," JRFM, MDPI, vol. 17(4), pages 1-25, April.

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    More about this item

    Keywords

    asset pricing; finite samples; identification robust inference; risk premia;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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