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Testing Beta-Pricing Models Using Large Cross-Sections

Author

Listed:
  • Valentina Raponi
  • Cesare Robotti
  • Paolo Zaffaroni
  • Andrew Karolyi

Abstract

We propose a methodology for estimating and testing beta-pricing models when a large number of assets is available for investment but the number of time-series observations is fixed. We first consider the case of correctly specified models with constant risk premia, and then extend our framework to deal with time-varying risk premia, potentially misspecified models, firm characteristics, and unbalanced panels. We show that our large cross-sectional framework poses a serious challenge to common empirical findings regarding the validity of beta-pricing models. In the context of pricing models with Fama-French factors, firm characteristics are found to explain a much larger proportion of variation in estimated expected returns than betas.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

  • Valentina Raponi & Cesare Robotti & Paolo Zaffaroni & Andrew Karolyi, 2020. "Testing Beta-Pricing Models Using Large Cross-Sections," The Review of Financial Studies, Society for Financial Studies, vol. 33(6), pages 2796-2842.
    • Handle: RePEc:oup:rfinst:v:33:y:2020:i:6:p:2796-2842.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhz064
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    Citations

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    Cited by:

    1. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    2. Allen, David, 2022. "Asset Pricing Tests, Endogeneity issues and Fama-French factors," MPRA Paper 113610, University Library of Munich, Germany.
    3. Chaieb, Ines & Langlois, Hugues & Scaillet, Olivier, 2021. "Factors and risk premia in individual international stock returns," Journal of Financial Economics, Elsevier, vol. 141(2), pages 669-692.
    4. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2023. "Latent Factor Analysis in Short Panels," Swiss Finance Institute Research Paper Series 23-44, Swiss Finance Institute.
    5. Beaulieu, Marie-Claude & Dufour, Jean-Marie & Khalaf, Lynda & Melin, Olena, 2023. "Identification-robust beta pricing, spanning, mimicking portfolios, and the benchmark neutrality of catastrophe bonds," Journal of Econometrics, Elsevier, vol. 236(1).
    6. Paolo Zaffaroni, 2023. "Comment on: Identification Robust Testing of Risk Premia in Finite Samples," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 303-305.
    7. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.
    8. Frank Kleibergen & Lingwei Kong & Zhaoguo Zhan, 2023. "Rejoinder on: Identification Robust Testing of Risk Premia in Finite Samples," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 311-315.
    9. Anatolyev, Stanislav & Mikusheva, Anna, 2022. "Factor models with many assets: Strong factors, weak factors, and the two-pass procedure," Journal of Econometrics, Elsevier, vol. 229(1), pages 103-126.
    10. Hollstein, Fabian & Prokopczuk, Marcel, 2022. "Testing Factor Models in the Cross-Section," Journal of Banking & Finance, Elsevier, vol. 145(C).

    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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