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Robust Bayesian Portfolio Choices

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  • Evan W. Anderson
  • Ai-Ru (Meg) Cheng

Abstract

We propose a Bayesian-averaging portfolio choice strategy with excellent out-of-sample performance. Every period a new model is born that assumes means and covariances are constant over time. Each period we estimate model parameters, update model probabilities, and compute robust portfolio choices by taking into account model uncertainty, parameter uncertainty, and non-stationarity. The portfolio choices achieve higher out-of-sample Sharpe ratios and certainty equivalents than rolling window schemes, the 1/N approach, and other leading strategies do on a majority of 24 datasets. Received September 8, 2012; accepted October 18, 2015 by Editor Pietro Veronesi.

Suggested Citation

  • Evan W. Anderson & Ai-Ru (Meg) Cheng, 2016. "Robust Bayesian Portfolio Choices," The Review of Financial Studies, Society for Financial Studies, vol. 29(5), pages 1330-1375.
    • Handle: RePEc:oup:rfinst:v:29:y:2016:i:5:p:1330-1375.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhw001
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    Citations

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    Cited by:

    1. Kellerer, Belinda, 2019. "Portfolio Optimization and Ambiguity Aversion," Junior Management Science (JUMS), Junior Management Science e. V., vol. 4(3), pages 305-338.
    2. Joscha Beckmann & Gary Koop & Dimitris Korobilis & Rainer Alexander Schüssler, 2020. "Exchange rate predictability and dynamic Bayesian learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 410-421, June.
    3. Doron Avramov & Si Cheng & Lior Metzker & Stefan Voigt, 2023. "Integrating Factor Models," Journal of Finance, American Finance Association, vol. 78(3), pages 1593-1646, June.
    4. Guo, Ming & Ou-Yang, Hui, 2021. "Alpha decay and Sharpe ratio: Two measures of investor performance," Economic Modelling, Elsevier, vol. 104(C).
    5. 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.
    6. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2018. "Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions," Journal of Econometrics, Elsevier, vol. 207(1), pages 71-91.
    7. Evan Anderson & Ai-ru (Meg) Cheng, 2022. "Portfolio Choices with Many Big Models," Management Science, INFORMS, vol. 68(1), pages 690-715, January.
    8. Fuertes, Ana-Maria & Zhao, Nan, 2023. "A Bayesian perspective on commodity style integration," Journal of Commodity Markets, Elsevier, vol. 30(C).
    9. Angelini, Pierpaolo & Maturo, Fabrizio, 2022. "The price of risk based on multilinear measures," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 39-57.
    10. Kouaissah, Noureddine, 2023. "Robust reward-risk performance measures with weakly second-order stochastic dominance constraints," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 53-62.
    11. Johannes Bock, 2018. "An updated review of (sub-)optimal diversification models," Papers 1811.08255, arXiv.org.
    12. Kouaissah, Noureddine, 2021. "Robust conditional expectation reward–risk performance measures," Economics Letters, Elsevier, vol. 202(C).
    13. Carrasco, Ignacio & Hansen, Erwin, 2022. "Asset pricing model uncertainty and portfolio choice," Finance Research Letters, Elsevier, vol. 45(C).

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