IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i17p3776-d1231782.html
   My bibliography  Save this article

Estimating the Capital Asset Pricing Model with Many Instruments: A Bayesian Shrinkage Approach

Author

Listed:
  • Cássio Roberto de Andrade Alves

    (Department of Economics, School of Economics, Business Administration and Accounting at Ribeirão Preto (FEA-RP/USP), University of São Paulo, Av. dos Bandeirantes 3900, Ribeirão Preto 14040-905, SP, Brazil
    These authors contributed equally to this work.)

  • Márcio Laurini

    (Department of Economics, School of Economics, Business Administration and Accounting at Ribeirão Preto (FEA-RP/USP), University of São Paulo, Av. dos Bandeirantes 3900, Ribeirão Preto 14040-905, SP, Brazil
    These authors contributed equally to this work.)

Abstract

This paper introduces an instrumental variable Bayesian shrinkage approach specifically designed for estimating the capital asset pricing model (CAPM) while utilizing a large number of instruments. Our methodology incorporates horseshoe, Laplace, and factor-based shrinkage priors to construct Bayesian estimators for CAPM, accounting for the presence of measurement errors. Through the use of simulated data, we illustrate the potential of our approach in mitigating the bias arising from errors-in-variables. Importantly, the conventional two-stage least squares estimation of the CAPM beta is shown to experience bias escalation as the number of instruments increases. In contrast, our approach effectively counters this bias, particularly in scenarios with a substantial number of instruments. In an empirical application using real-world data, our proposed methodology generates subtly distinct estimated CAPM beta values compared with both the ordinary least squares and the two-stage least squares approaches. This disparity in estimations carries notable economic implications. Furthermore, when applied to average cross-sectional asset returns, our approach significantly enhances the explanatory power of the CAPM framework.

Suggested Citation

  • Cássio Roberto de Andrade Alves & Márcio Laurini, 2023. "Estimating the Capital Asset Pricing Model with Many Instruments: A Bayesian Shrinkage Approach," Mathematics, MDPI, vol. 11(17), pages 1-20, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3776-:d:1231782
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/17/3776/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/17/3776/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Hotz‐Behofsits & Florian Huber & Thomas Otto Zörner, 2018. "Predicting crypto‐currencies using sparse non‐Gaussian state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 627-640, September.
    2. Brignone, Riccardo & Gonzato, Luca & Lütkebohmert, Eva, 2023. "Efficient Quasi-Bayesian Estimation of Affine Option Pricing Models Using Risk-Neutral Cumulants," Journal of Banking & Finance, Elsevier, vol. 148(C).
    3. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, January.
    4. Zhimin (Jimmy) Yu, 2023. "Cross-Section of Returns, Predictors Credibility, and Method Issues," JRFM, MDPI, vol. 16(1), pages 1-12, January.
    5. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    6. Daniel R. Kowal & David S. Matteson & David Ruppert, 2019. "Dynamic shrinkage processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(4), pages 781-804, September.
    7. P. Richard Hahn & Jingyu He & Hedibert Lopes, 2018. "Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 278-287, April.
    8. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    9. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    10. Martin Lettau & Sydney Ludvigson, 2001. "Resurrecting the (C)CAPM: A Cross-Sectional Test When Risk Premia Are Time-Varying," Journal of Political Economy, University of Chicago Press, vol. 109(6), pages 1238-1287, December.
    11. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    12. Gibbons, Michael R & Ross, Stephen A & Shanken, Jay, 1989. "A Test of the Efficiency of a Given Portfolio," Econometrica, Econometric Society, vol. 57(5), pages 1121-1152, September.
    13. 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.
    14. Fisher, Mark & Jensen, Mark J., 2022. "Bayesian nonparametric learning of how skill is distributed across the mutual fund industry," Journal of Econometrics, Elsevier, vol. 230(1), pages 131-153.
    15. Hansen, Christian & Hausman, Jerry & Newey, Whitney, 2008. "Estimation With Many Instrumental Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 398-422.
    16. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    17. Fuller, Wayne A, 1977. "Some Properties of a Modification of the Limited Information Estimator," Econometrica, Econometric Society, vol. 45(4), pages 939-953, May.
    18. Bekker, Paul A, 1994. "Alternative Approximations to the Distributions of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 62(3), pages 657-681, May.
    19. Cai, Zongwu & Fang, Ying & Xu, Qiuhua, 2022. "Testing capital asset pricing models using functional-coefficient panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 227(1), pages 114-133.
    20. 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.
    21. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    22. Pankaj Agrrawal, 2023. "The Gibbons, Ross, and Shanken Test for Portfolio Efficiency: A Note Based on Its Trigonometric Properties," Mathematics, MDPI, vol. 11(9), pages 1-19, May.
    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. J. Ginger Meng & Gang Hu & Jushan Bai, 2011. "Olive: A Simple Method For Estimating Betas When Factors Are Measured With Error," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 34(1), pages 27-60, March.
    2. 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.
    3. Adrian, Tobias & Franzoni, Francesco, 2009. "Learning about beta: Time-varying factor loadings, expected returns, and the conditional CAPM," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 537-556, September.
    4. Vendrame, Vasco & Guermat, Cherif & Tucker, Jon, 2018. "A conditional regime switching CAPM," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 1-11.
    5. Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    6. Ludvigson, Sydney C., 2013. "Advances in Consumption-Based Asset Pricing: Empirical Tests," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 799-906, Elsevier.
    7. Committee, Nobel Prize, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    8. Da, Zhi & Guo, Re-Jin & Jagannathan, Ravi, 2012. "CAPM for estimating the cost of equity capital: Interpreting the empirical evidence," Journal of Financial Economics, Elsevier, vol. 103(1), pages 204-220.
    9. Kolari, James W. & Huang, Jianhua Z. & Butt, Hilal Anwar & Liao, Huiling, 2022. "International tests of the ZCAPM asset pricing model," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    10. Constantinos Antoniou & John A. Doukas & Avanidhar Subrahmanyam, 2016. "Investor Sentiment, Beta, and the Cost of Equity Capital," Management Science, INFORMS, vol. 62(2), pages 347-367, February.
    11. Ciciretti, Rocco & Dalò, Ambrogio & Dam, Lammertjan, 2023. "The contributions of betas versus characteristics to the ESG premium," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 104-124.
    12. Yu Wang & Haicheng Shu, 2019. "Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China," Working Papers 2019-10-10, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    13. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    14. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    15. De Moor, Lieven & Sercu, Piet, 2013. "The smallest firm effect: An international study," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 129-155.
    16. Javid, Attiya Yasmin & Ahmad, Eatzaz, 2008. "Testing multifactor capital asset pricing model in case of Pakistani market," MPRA Paper 37341, University Library of Munich, Germany.
    17. Cynthia M. Gong & Di Luo & Huainan Zhao, 2021. "Liquidity risk and the beta premium," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(4), pages 789-814, December.
    18. Murtazashvili, Irina & Vozlyublennaia, Nadia, 2012. "The role of data limitations, seasonality and frequency in asset pricing models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 555-574.
    19. Sebastien Valeyre & Sofiane Aboura & Denis Grebenkov, 2019. "The Reactive Beta Model," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 42(1), pages 71-113, March.
    20. De Giorgi, Enrico G. & Post, Thierry & Yalçın, Atakan, 2019. "A concave security market line," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 65-81.

    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:jmathe:v:11:y:2023:i:17:p:3776-:d:1231782. 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.