This study contributes to the growing literature that focuses on predicting crude oil spot price returns out-of-sample by conditioning on the news-based uncertainty measures pioneered by Baker et al. (2016). With the aim of providing new empirical results useful for future research, we apply a comprehensive Bayesian model averaging (BMA) framework that incorporates the following aspects: (i): Parameter instability, (ii): Model uncertainty, and (iii): Besides the conditional mean process, it allows predictors of the candidate models in the model set to impact the variable being predicted through the conditional volatility process or both processes. Applied to monthly news-based uncertainty and crude oil price data from $1985m1$ through $2020m12$, we observe that accounting for model uncertainty and allowing predictors to impact crude oil price returns exclusively through the conditional volatility process lead to the most consistent pattern of point (density) prediction accuracy gains relative to the benchmark. In contrast, the approach predominately relied on in the current literature, namely, allowing predictors to impact returns only through the conditional mean process does not lead to the same degree of point (density) prediction accuracy gains. Likewise, any further prediction accuracy gains from (i) are at best modest once (ii) and (iii) are accounted for. The largest relative gain occurs when predicting the left tail of the conditional return distribution one-month ahead. The statistical evidence of predictability also translates to economic gains.

这项研究为越来越多的文献做出贡献，这些文献侧重于通过调整 Baker 等人开创的基于新闻的不确定性措施来预测样本外原油现货价格回报。(2016)。为了提供对未来研究有用的新实证结果，我们应用了综合贝叶斯模型平均 (BMA) 框架，该框架包含以下方面：(i)：参数不稳定性，(ii)：模型不确定性，以及 (iii)：此外条件平均过程，它允许模型集中候选模型的预测变量通过条件波动过程或两个过程影响被预测的变量。应用于每月基于新闻的不确定性和原油价格数据$\mathrm{1985年}米1$ 通过 $\mathrm{2020年}米12$，我们观察到考虑模型的不确定性并允许预测变量专门影响原油价格回报通过条件波动过程导致相对于基准的最一致的点（密度）预测精度增益模式。相比之下，当前文献中主要依赖的方法，即允许预测变量仅通过条件平均过程影响回报不会导致相同程度的点（密度）预测精度增益。同样，一旦考虑到 (ii) 和 (iii)，来自 (i) 的任何进一步的预测准确度增益充其量只是适度的。最大的相对收益发生在提前一个月预测条件回报分布的左尾时。可预测性的统计证据也转化为经济收益。