Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach
构建了一个投资者具有多先验和模糊厌恶的模型，通过预期收益的置信区间刻画多先验，并利用最小化先验来建模模糊厌恶。该模型具有公理基础，能灵活处理不同资产子集预期收益的不确定性，并给出最优投资组合的闭式解。实证表明，模糊厌恶组合比经典和贝叶斯模型更稳定，且样本外夏普比率更高。
We develop a model for an investor with multiple priors and aversion to ambiguity. We characterize the multiple priors by a "confidence interval" around the estimated expected returns and we model ambiguity aversion via a minimization over the priors. Our model has several attractive features: (1) it has a solid axiomatic foundation; (2) it is flexible enough to allow for different degrees of uncertainty about expected returns for various subsets of assets and also about the return-generating model; and (3) it delivers closed-form expressions for the optimal portfolio. Our empirical analysis suggests that, compared with portfolios from classical and Bayesian models, ambiguity-averse portfolios are more stable over time and deliver a higher out-of sample Sharpe ratio. Copyright 2007, Oxford University Press.