IDEAS home Printed from https://ideas.repec.org/a/bla/acctfi/v60y2020i4p4147-4173.html
   My bibliography  Save this article

Estimating the rank of a beta matrix: a GMM approach

Author

Listed:
  • Yu Ren
  • Qin Wang

Abstract

A full‐rank beta matrix is a necessary condition for correctly estimating the risk premia in linear asset pricing models. However, the true values of betas are unobserved in practice and must be estimated. In this paper, we propose a straightforward testing method based on the generalised method of moments to assess whether the beta matrix is of full rank. We show that our method has desirable finite sample properties and performs better than available alternatives. We apply our method to several popular factor models and find that most models have rank deficiency in several datasets.

Suggested Citation

  • Yu Ren & Qin Wang, 2020. "Estimating the rank of a beta matrix: a GMM approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 4147-4173, December.
  • Handle: RePEc:bla:acctfi:v:60:y:2020:i:4:p:4147-4173
    DOI: 10.1111/acfi.12480
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/acfi.12480
    Download Restriction: no

    File URL: https://libkey.io/10.1111/acfi.12480?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cragg, John G. & Donald, Stephen G., 1997. "Inferring the rank of a matrix," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 223-250.
    2. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    3. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    4. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    5. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    6. Kleibergen, Frank & Paap, Richard, 2006. "Generalized reduced rank tests using the singular value decomposition," Journal of Econometrics, Elsevier, vol. 133(1), pages 97-126, July.
    7. Jagannathan, Ravi & Skoulakis, Georgios & Wang, Zhenyu, 2002. "Generalized Method of Moments: Applications in Finance," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 470-481, October.
    8. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    9. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    10. Kewei Hou & Chen Xue & Lu Zhang, 2015. "Editor's Choice Digesting Anomalies: An Investment Approach," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 650-705.
    11. Raymond Kan & Chu Zhang, 1999. "Two‐Pass Tests of Asset Pricing Models with Useless Factors," Journal of Finance, American Finance Association, vol. 54(1), pages 203-235, February.
    12. MacKinlay, A Craig & Richardson, Matthew P, 1991. "Using Generalized Method of Moments to Test Mean-Variance Efficiency," Journal of Finance, American Finance Association, vol. 46(2), pages 511-527, June.
    13. Craig Burnside, 2016. "Identification and Inference in Linear Stochastic Discount Factor Models with Excess Returns," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 295-330.
    14. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    15. Titman, Sheridan & Wei, K. C. John & Xie, Feixue, 2004. "Capital Investments and Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 677-700, December.
    16. Robert F. Stambaugh & Jianfeng Yu & Yu Yuan, 2015. "Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle," Journal of Finance, American Finance Association, vol. 70(5), pages 1903-1948, October.
    17. Ahn, Seung C. & Horenstein, Alex R. & Wang, Na, 2018. "Beta Matrix and Common Factors in Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1417-1440, June.
    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. 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. Virk, Nader Shahzad & Butt, Hilal Anwar, 2022. "Asset pricing anomalies: Liquidity risk hedgers or liquidity risk spreaders?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    3. Doron Avramov & Tarun Chordia & Gergana Jostova & Alexander Philipov, 2022. "The Distress Anomaly is Deeper than You Think: Evidence from Stocks and Bonds [The prediction of corporate bankruptcy: a discriminant analysis]," Review of Finance, European Finance Association, vol. 26(2), pages 355-405.
    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. Robert F. Stambaugh & Yu Yuan, 2017. "Mispricing Factors," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1270-1315.
    6. Jang, Jeewon & Kang, Jangkoo, 2019. "Probability of price crashes, rational speculative bubbles, and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 222-247.
    7. Mateus, Irina B. & Mateus, Cesario & Todorovic, Natasa, 2019. "Review of new trends in the literature on factor models and mutual fund performance," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 344-354.
    8. Stefan Nagel, 2013. "Empirical Cross-Sectional Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 5(1), pages 167-199, November.
    9. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, September.
    10. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    11. Xin Chen & Wei He & Libin Tao & Jianfeng Yu, 2023. "Attention and Underreaction-Related Anomalies," Management Science, INFORMS, vol. 69(1), pages 636-659, January.
    12. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    13. Kent Daniel & David Hirshleifer & Lin Sun, 2020. "Short- and Long-Horizon Behavioral Factors," The Review of Financial Studies, Society for Financial Studies, vol. 33(4), pages 1673-1736.
    14. Hou, Kewei & Xue, Chen & Zhang, Lu, 2017. "Replicating Anomalies," Working Paper Series 2017-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    15. Liu, Sha & Han, Jingguang, 2020. "Media tone and expected stock returns," International Review of Financial Analysis, Elsevier, vol. 70(C).
    16. Sun, Yang & Zhang, Xuan & Zhang, Zhekai, 2022. "The reduced-rank beta in linear stochastic discount factor models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    17. Kim, Dongcheol & Lee, Inro & Na, Haejung, 2019. "Financial distress, short sale constraints, and mispricing," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 94-111.
    18. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    19. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    20. Bali, Turan G. & Weigert, Florian, 2021. "Hedge funds and the positive idiosyncratic volatility effect," CFR Working Papers 21-01, University of Cologne, Centre for Financial Research (CFR).

    More about this item

    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:bla:acctfi:v:60:y:2020:i:4:p:4147-4173. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/aaanzea.html .

    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.