IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/2317.html
   My bibliography  Save this paper

The Role of Pricing Errors in Linear Asset Pricing Models with Strong, Semi-strong, and Latent Factors

Author

Listed:
  • Pesaran, M. H.
  • Smith, R. P.

Abstract

This paper examines the role of pricing errors in linear factor pricing models, allowing for observed strong and semi-strong factors, and latent weak factors. It focusses on the estimation of Φk = λk - μk which plays a pivotal role, not only in the estimation of risk premia but also in tests of market efficiency, where λk and μk are respectively the risk premium and the mean of the kth risk factor. It proposes a two-step estimator of Φk with Shanken type bias-correction, and derives its asymptotic distribution under a general setting that allows for idiosyncratic pricing errors, weak missing factors, as well as weak error cross-sectional dependence. The implications of semi-strong factors for the asymptotic distribution of the proposed estimator are also investigated. Small sample results from extensive Monte Carlo experiments show that the proposed estimator has the correct size with good power properties. The paper also provides an empirical application to a large number of U.S. securities with risk factors selected from a large number of potential risk factors according to their strength.

Suggested Citation

  • Pesaran, M. H. & Smith, R. P., 2023. "The Role of Pricing Errors in Linear Asset Pricing Models with Strong, Semi-strong, and Latent Factors," Cambridge Working Papers in Economics 2317, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2317
    Note: mhp1
    as

    Download full text from publisher

    File URL: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2317.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    2. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    3. Bailey, Natalia & Pesaran, M. Hashem & Smith, L. Vanessa, 2019. "A multiple testing approach to the regularisation of large sample correlation matrices," Journal of Econometrics, Elsevier, vol. 208(2), pages 507-534.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M Hashem Pesaran & Takashi Yamagata, 2024. "Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 407-460.
    2. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    3. Evaluator 1, 2024. "Evaluation 1 of "Biodiversity Risk"," The Unjournal Evaluations 2024-151, The Unjournal.
    4. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    5. Alexander Chudik & M. Hashem Pesaran & Kamiar Mohaddes, 2020. "Identifying Global and National Output and Fiscal Policy Shocks Using a GVAR," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 143-189, Emerald Group Publishing Limited.
    6. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    7. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    8. Tae-Hwy Lee & Millie Yi Mao & Aman Ullah, 2021. "Estimation of high-dimensional dynamic conditional precision matrices with an application to forecast combination," Econometric Reviews, Taylor & Francis Journals, vol. 40(10), pages 905-918, November.
    9. Alexander Chudik & M. Hashem Pesaran & Ron P. Smith, 2023. "Revisiting the Great Ratios Hypothesis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 1023-1047, October.
    10. Julien Acalin & Alessandro Rebucci, 2020. "Global Business and Financial Cycles: A Tale of Two Capital Account Regimes," NBER Working Papers 27739, National Bureau of Economic Research, Inc.
    11. Elhorst, J. Paul & Gross, Marco & Tereanu, Eugen, 2018. "Spillovers in space and time: where spatial econometrics and Global VAR models meet," Working Paper Series 2134, European Central Bank.
    12. Pesaran, M. Hashem & Smith, Ron P., 2023. "Arbitrage pricing theory, the stochastic discount factor and estimation of risk premia from portfolios," Econometrics and Statistics, Elsevier, vol. 26(C), pages 17-30.
    13. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
    14. M. Hashem Pesaran & Ron P. Smith, 2021. "Factor Strengths, Pricing Errors, and Estimation of Risk Premia," CESifo Working Paper Series 8947, CESifo.
    15. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    16. Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2020. "Variable Selection in High Dimensional Linear Regressions with Parameter Instability," Globalization Institute Working Papers 394, Federal Reserve Bank of Dallas, revised 05 Aug 2024.
    17. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    18. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    19. Nusret Cakici & Christian Fieberg & Daniel Metko & Adam Zaremba, 2024. "Do Anomalies Really Predict Market Returns? New Data and New Evidence," Review of Finance, European Finance Association, vol. 28(1), pages 1-44.
    20. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2019. "Exponent of Cross-sectional Dependence for Residuals," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 46-102, September.

    More about this item

    Keywords

    Factor strength; pricing errors; risk premia; missing factors; Fama-French factors; panel R2;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cam:camdae:2317. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

    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.