This study revisits the topic of predicting aggregate equity returns out-of-sample by conditioning on economic variables through Bayesian model averaging (BMA). Besides simultaneously addressing parameter instability and model uncertainty, I suggest a new model feature, namely, predictors in a given model can also impact the dependent variable through the conditional volatility process. The suggested econometric framework is straightforward to implement without requiring simulation. Likewise, the user can easily decide, which aspects of the predictive channel should to be switched on, off or altered. I apply the suggested framework to the well-known [Goyal, A. and Welch, I., A comprehensive look at the empirical performance of equity premium prediction. Rev. Financial Stud., 2008, 21, 1455–1508] dataset. An extensive out-of-sample prediction evaluation demonstrates that averaging over predictor combinations in a model that allows lagged predictors to impact aggregate equity returns exclusively through the conditional volatility process results in statistically significant more accurate density predictions relative to the benchmark, especially when predicting the left tail of the conditional distribution. One also observes economic gains in favor of certain BMAs. Here, the BMA that allows predictors to impact equity returns through the conditional mean as well as the conditional volatility process is the top performer.

本研究重新探讨了通过贝叶斯模型平均 (BMA) 对经济变量进行调节来预测样本外总股权回报的主题。除了同时解决参数不稳定性和模型不确定性之外，我还提出了一个新的模型特征，即给定模型中的预测变量也可以通过条件波动过程影响因变量。建议的计量经济学框架无需模拟即可直接实施。同样，用户可以轻松决定应该打开、关闭或更改预测频道的哪些方面。我将建议的框架应用于著名的 [Goyal, A. 和 Welch, I.，对股权溢价预测的实证表现的全面观察。牧师金融研究。, 2008, 21, 1455–1508] 数据集。广泛的样本外预测评估表明，在模型中对预测变量组合进行平均，该模型允许滞后预测变量仅通过条件波动率过程影响总股票收益，从而产生相对于基准具有统计意义的更准确的密度预测，尤其是在预测条件分布的左尾。人们还观察到有利于某些 BMA 的经济收益。在这里，允许预测变量通过条件均值和条件波动率过程影响股票收益的 BMA 表现最佳。