论文标题
贝叶斯对回报可预测性的对帐
Bayesian Reconciliation of Return Predictability
论文作者
论文摘要
本文考虑了稳定的矢量自回旋（VAR）模型，并研究了贝叶斯环境中的返回可预测性。 VAR系统包括Cochrane（2008）中提出的资产返回和股息价格比率，并允许将回报可预测性的问题固定到一个特定模型参数的值中。我们开发了此参数的新收缩类型，并将我们的贝叶斯方法与普通最小二乘估计以及Amihud和Hurvich（2004）中提出的偏差估计量进行比较。一项仿真研究表明，贝叶斯方法在观察到的大小（假阳性）和功率（假阴性）方面主导了偏差估计量的减少。我们将方法分别应用于1926年至2004年和从1953年至2021年的年度CRSP价值加权收益。对于第一个样本，贝叶斯方法支持无回报可预测性的假设，而对于第二个数据集，则观察到第二个数据集的可预测性证据。
This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The VAR system comprises asset returns and the dividend-price ratio as proposed in Cochrane (2008), and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed.
