IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i4p168-d1379260.html
   My bibliography  Save this article

Testing and Ranking of Asset Pricing Models Using the GRS Statistic

Author

Listed:
  • Mark J. Kamstra

    (Schulich School of Business, Room N204-C, York University, 4700 Keele St., Toronto, ON M3J 1P3, Canada
    These authors contributed equally to this work.)

  • Ruoyao Shi

    (Department of Economics, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
    These authors contributed equally to this work.)

Abstract

We clear up an ambiguity in the statement of the GRS statistic by providing the correct formula of the GRS statistic and the first proof of its F-distribution in the general multiple-factor case. Casual generalization of the Sharpe-ratio-based interpretation of the single-factor GRS statistic to the multiple-portfolio case makes experts in asset pricing studies susceptible to an incorrect formula. We illustrate the consequences of using the incorrect formulas that the ambiguity in GRS leads to—over-rejecting and misranking asset pricing models. In addition, we suggest a new approach to ranking models using the GRS statistic p -value.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:4:p:168-:d:1379260
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/4/168/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/4/168/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kan, Raymond & Wang, Xiaolu & Zheng, Xinghua, 2024. "In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models," Journal of Financial Economics, Elsevier, vol. 155(C).
    2. K. Geert Rouwenhorst, 1999. "Local Return Factors and Turnover in Emerging Stock Markets," Journal of Finance, American Finance Association, vol. 54(4), pages 1439-1464, August.
    3. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    4. Affleck-Graves, John & McDonald, Bill, 1989. " Nonnormalities and Tests of Asset Pricing Theories," Journal of Finance, American Finance Association, vol. 44(4), pages 889-908, September.
    5. Frank Kleibergen & Zhaoguo Zhan, 2020. "Robust Inference for Consumption‐Based Asset Pricing," Journal of Finance, American Finance Association, vol. 75(1), pages 507-550, February.
    6. 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.
    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. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    2. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, October.
    3. Ray Ball & Gil Sadka & Ayung Tseng, 2022. "Using accounting earnings and aggregate economic indicators to estimate firm-level systematic risk," Review of Accounting Studies, Springer, vol. 27(2), pages 607-646, June.
    4. Hollstein, Fabian & Prokopczuk, Marcel, 2022. "Testing Factor Models in the Cross-Section," Journal of Banking & Finance, Elsevier, vol. 145(C).
    5. Zhang, Xiang & Liu, Yangyi & Wu, Kun & Maillet, Bertrand, 2021. "Tradable or nontradable factors—what does the Hansen–Jagannathan distance tell us?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 853-879.
    6. Bartram, Söhnke M. & Grinblatt, Mark, 2021. "Global market inefficiencies," Journal of Financial Economics, Elsevier, vol. 139(1), pages 234-259.
    7. Dierkes, Maik & Krupski, Jan, 2022. "Isolating momentum crashes," Journal of Empirical Finance, Elsevier, vol. 66(C), pages 1-22.
    8. Vibhuti Vasishth & Sanjay Sehgal & Gagan Sharma, 2021. "Size Effect in Indian Equity Market: Myth or Reality?," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 101-119, March.
    9. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
    10. Qiao, Zhuo & Wang, Yan & Lam, Keith S.K., 2022. "New evidence on Bayesian tests of global factor pricing models," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 160-172.
    11. 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.
    12. Harshita & Shveta Singh & Surendra S. Yadav, 2018. "Changing Nature of the Value Premium in the Indian Stock Market," Vision, , vol. 22(2), pages 135-143, June.
    13. Hollstein, Fabian, 2022. "The world of anomalies: Smaller than we think?," Journal of International Money and Finance, Elsevier, vol. 129(C).
    14. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    15. Li, Kai, 2021. "Nonlinear effect of sentiment on momentum," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    16. Chang, Ran & Gonzalez, Angelica & Sarkissian, Sergei & Tu, Jun, 2022. "Internal capital markets and predictability in complex ownership firms," Journal of Corporate Finance, Elsevier, vol. 74(C).
    17. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    18. Huang, Alex YiHou, 2024. "Mechanisms of overpricing: An investigation on momentum crashes," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 118-142.
    19. Julio Lobao & Joao Meira Fernandes, 2017. "The 52-Week High and Momentum Investing: Implications for Asset Pricing Models," Annals of Economics and Finance, Society for AEF, vol. 18(2), pages 349-376, November.
    20. Robert Snigaroff & David Wroblewski, 2023. "Consumption with earnings, liquidity, and market based models," Review of Quantitative Finance and Accounting, Springer, vol. 60(2), pages 501-530, February.

    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:gam:jjrfmx:v:17:y:2024:i:4:p:168-:d:1379260. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.