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Uncertainty in the Black–Litterman model: Empirical estimation of the equilibrium

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  • Fuhrer, Adrian
  • Hock, Thorsten

Abstract

The Black–Litterman model is a widely used and well established application of the Bayesian framework to asset allocation problems. It is, however, difficult to calibrate, as it requires the specification of abstract uncertainty parameters. We propose a new, more flexible model that allows the empirical estimation of the equilibrium, alleviating the need for parametrization. In an empirical application, we illustrate the sensitivity of the classical Black–Litterman model to the choice of the uncertainty parameter. We then demonstrate that the flexible model successfully exploits information in the cross-section of index constituents’ returns to find an optimal trade-off in calibration of the uncertainty.

Suggested Citation

  • Fuhrer, Adrian & Hock, Thorsten, 2023. "Uncertainty in the Black–Litterman model: Empirical estimation of the equilibrium," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 251-275.
    • Handle: RePEc:eee:empfin:v:72:y:2023:i:c:p:251-275
    DOI: 10.1016/j.jempfin.2023.03.009
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    References listed on IDEAS

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    More about this item

    Keywords

    Asset allocation; Bayesian; Black–Litterman model; Error components models; Model uncertainty; Portfolio choice;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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