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Too Good to Be True? Fallacies in Evaluating Risk Factor Models

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  • Nikolay Gospodinov
  • Raymond Kan
  • Cesare Robotti

Abstract

This paper is concerned with statistical inference and model evaluation in possibly misspecified and unidentified linear asset-pricing models estimated by maximum likelihood and one-step generalized method of moments. Strikingly, when spurious factors (that is, factors that are uncorrelated with the returns on the test assets) are present, the models exhibit perfect fit, as measured by the squared correlation between the model's fitted expected returns and the average realized returns. Furthermore, factors that are spurious are selected with high probability, while factors that are useful are driven out of the model. Although ignoring potential misspecification and lack of identification can be very problematic for models with macroeconomic factors, empirical specifications with traded factors (e.g., Fama and French, 1993, and Hou, Xue, and Zhang, 2015) do not suffer of the identification problems documented in this study.

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  • Nikolay Gospodinov & Raymond Kan & Cesare Robotti, 2017. "Too Good to Be True? Fallacies in Evaluating Risk Factor Models," FRB Atlanta Working Paper 2017-9, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:2017-09
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    Cited by:

    1. Gospodinov, Nikolay & Kan, Raymond & Robotti, Cesare, 2019. "Too good to be true? Fallacies in evaluating risk factor models," Journal of Financial Economics, Elsevier, vol. 132(2), pages 451-471.
    2. Gospodinov, Nikolay & Robotti, Cesare, 2021. "Common pricing across asset classes: Empirical evidence revisited," Journal of Financial Economics, Elsevier, vol. 140(1), pages 292-324.
    3. Marie-Claude Beaulieu & Jean-Marie Dufour & Lynda Khalaf, 2020. "Arbitrage Pricing, Weak Beta, Strong Beta: Identification-Robust and Simultaneous Inference," Cahiers de recherche 15-2020, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    4. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    5. Jamali, Ibrahim & Yamani, Ehab & Smallwood, Aaron D., 2023. "An investment-based explanation of currency excess returns," Journal of International Money and Finance, Elsevier, vol. 133(C).
    6. Manresa, Elena & Peñaranda, Francisco & Sentana, Enrique, 2023. "Empirical evaluation of overspecified asset pricing models," Journal of Financial Economics, Elsevier, vol. 147(2), pages 338-351.
    7. Raymond Kan & Cesare Robotti, 0. "Comment on: Pseudo-True SDFs in Conditional Asset Pricing Models," Journal of Financial Econometrics, Oxford University Press, vol. 18(4), pages 729-735.
    8. Lin, Qi, 2021. "The q5 model and its consistency with the intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 127(C).
    9. Hansen, Erwin, 2022. "Economic evaluation of asset pricing models under predictability," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 50-66.
    10. Zhang, Xiang, 2020. "Leisure and long-run risks: An empirical evaluation on value premium puzzle," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).

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    More about this item

    Keywords

    asset pricing; spurious risk factors; unidentified models; model misspecification; continuously updated GMM; maximum likelihood; goodness-of-fit; rank test;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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