Review of Financial Studies, May 2016, v. 29, iss. 5, pp. 1330-75

稳健的贝叶斯投资组合选择

作者：Anderson, Evan W. (Northern IL U), Cheng, Ai-Ru (Northern IL U)

摘要：我们提出了一个贝叶斯平均投资组合选择策略（Bayesian-averaging portfolio choice strategy），该策略有着非常好的样本外表现。每个阶段都会产生一个新的模型，它假设均值和方差一直不变。在每个阶段，我们估计模型参数，更新模型概率，并且通过考虑模型的不确定性、参数的不确定性和不稳定性来计算稳健的投资组合选择。相较于滚动窗口方案、1/N方法以及其他领先战略，这种投资组合选择策略在24个数据集的大部分中都实现了更高的样本外夏普比率和确定等值。

Robust Bayesian Portfolio Choices

Anderson, Evan W. (Northern IL U), Cheng, Ai-Ru (Northern IL U)

ABSTRACT

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.

原文链接：

http://rfs.oxfordjournals.org/content/29/5/1330.abstract

翻译：孙雨琦