Robust Bayesian Portfolio Choices
提出一种贝叶斯平均投资组合策略，通过考虑模型不确定性、参数不确定性和非平稳性，在多数数据集上实现了比滚动窗口、等权重等策略更高的样本外夏普比率和确定性等价收益。
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.