Uncertainty in the Black–Litterman model: Empirical estimation of the equilibrium
针对布莱克-利特曼模型难以校准不确定性参数的问题，提出一个更灵活的模型，允许对均衡进行实证估计，并通过实证应用展示新模型能利用指数成分股收益的横截面信息找到最优校准权衡。
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.