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

Adrian Fuhrer and Thorsten Hock

Journal of Empirical Finance, 2023, vol. 72, issue C, 251-275

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.

Keywords: Asset allocation; Bayesian; Black–Litterman model; Error components models; Model uncertainty; Portfolio choice (search for similar items in EconPapers)
JEL-codes: C11 C33 D84 G11 G14 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:72:y:2023:i:c:p:251-275

DOI: 10.1016/j.jempfin.2023.03.009

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