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Bayesian Portfolio Analysis

Author

Listed:
  • Doron Avramov
  • Guofu Zhou

    () (Finance Department, The Hebrew University of Jerusalem, Mt. Scopus Jerusalem 91905, Israel; R.H. Smith School of Business, University of Maryland, College Park, Maryland 20742
    Olin Business School, Washington University, St. Louis, Missouri 63130)

Abstract

This paper reviews the literature on Bayesian portfolio analysis. Information about events, macro conditions, asset pricing theories, and security-driving forces can serve as useful priors in selecting optimal portfolios. Moreover, parameter uncertainty and model uncertainty are practical problems encountered by all investors. The Bayesian framework neatly accounts for these uncertainties, whereas standard statistical models often ignore them. We review Bayesian portfolio studies when asset returns are assumed both independently and identically distributed as well as predictable through time. We cover a range of applications, from investing in single assets and equity portfolios to mutual and hedge funds. We also outline challenges for future work.

Suggested Citation

  • Doron Avramov & Guofu Zhou, 2010. "Bayesian Portfolio Analysis," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 25-47, December.
  • Handle: RePEc:anr:refeco:v:2:y:2010:p:25-47
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    References listed on IDEAS

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    Cited by:

    1. Li, Yong & Yu, Jun, 2012. "Bayesian hypothesis testing in latent variable models," Journal of Econometrics, Elsevier, vol. 166(2), pages 237-246.
    2. Sangwon Suh, 2016. "A Combination Rule for Portfolio Selection with Transaction Costs," International Review of Finance, International Review of Finance Ltd., vol. 16(3), pages 393-420, September.
    3. Erindi Allaj, 2013. "The Black–Litterman model: a consistent estimation of the parameter tau," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 27(2), pages 217-251, June.
    4. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2016. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 814-829.
    5. Jessica A. Wachter, 2010. "Asset Allocation," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 175-206, December.
    6. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    7. Bodnar, Taras & Mazur, Stepan & Okhrin, Yarema, 2017. "Bayesian estimation of the global minimum variance portfolio," European Journal of Operational Research, Elsevier, vol. 256(1), pages 292-307.
    8. repec:eee:jbfina:v:84:y:2017:i:c:p:68-87 is not listed on IDEAS
    9. Bauder, David & Bodnar, Taras & Mazur, Stepan & Okhrin, Yarema, 2018. "Bayesian inference for the tangent portfolio," Working Papers 2018:2, Örebro University, School of Business.
    10. repec:bap:journl:170303 is not listed on IDEAS
    11. David Bauder & Taras Bodnar & Nestor Parolya & Wolfgang Schmid, 2018. "Bayesian mean-variance analysis: Optimal portfolio selection under parameter uncertainty," Papers 1803.03573, arXiv.org.
    12. Gillen, Benjamin J., 2014. "An empirical Bayesian approach to stein-optimal covariance matrix estimation," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 402-420.
    13. Chiaki Hara & Toshiki Honda, 2014. "Asset Demand and Ambiguity Aversion," KIER Working Papers 911, Kyoto University, Institute of Economic Research.

    More about this item

    Keywords

    portfolio choice; parameter uncertainty; informative prior beliefs; return predictability; model uncertainty; learning;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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