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Bayesian Portfolio Analysis

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Author Info

  • 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.

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Bibliographic Info

Article provided by Annual Reviews in its journal Annual Review of Financial Economics.

Volume (Year): 2 (2010)
Issue (Month): 1 (December)
Pages: 25-47

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Handle: RePEc:anr:refeco:v:2:y:2010:p:25-47

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Related research

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

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References

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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. Erindi Allaj, 2013. "The Black–Litterman model: a consistent estimation of the parameter tau," Financial Markets and Portfolio Management, Springer, vol. 27(2), pages 217-251, June.

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