To improve the predictability of carbon prices under diversified attention, this paper develops a dynamic model averaging approach with common factors (DMA-CF) which uses dimension-reduction techniques to extract factors from all models including the subsets of attention predictors and allows time-varying coefficients and model switching. The in-sample results using univariate models reveal the strong predictive power of the attention measured by the Google search volume index (). The out-of-sample results confirm it as the strongest attention predictor, and show the superior performance of DMA-CFs relative to the original DMA and the benchmark. We further investigate how common factors improve the forecasting performance of DMA. The empirical evidence indicates that common factors efficiently aggregate the information and reduce the estimation errors in complicated models which are assigned with higher probability in DMA-CFs than in DMA, thereby digging out more predictive information for forecasting carbon prices. Moreover, DMA primarily depends on , with the highest weights, while DMA-CFs slightly downweight the and allocate more weight to the attention proxy of abnormal trading volume () which provides complementary information. Finally, our DMA-CF methods can improve the economic gains in the portfolio exercise with carbon futures.

中文翻译：

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多样化关注下的碳价格预测：具有公因子的动态模型平均法

为了提高分散注意力下碳价格的可预测性，本文开发了一种具有公共因素的动态模型平均方法（DMA-CF），该方法使用降维技术从包括注意力预测子集在内的所有模型中提取因素，并允许时变系数和模型切换。使用单变量模型的样本内结果揭示了通过谷歌搜索量指数（）测量的注意力的强大预测能力。样本外结果证实了它是最强的注意力预测器，并显示了 DMA-CF 相对于原始 DMA 和基准的优越性能。我们进一步研究常见因素如何提高 DMA 的预测性能。经验证据表明，公共因素有效地聚合了信息并减少了复杂模型中的估计误差，这些模型在DMA-CF中的分配概率高于DMA，从而挖掘出更多的预测信息来预测碳价格。此外，DMA 主要依赖于 ，权重最高，而 DMA-CF 稍微降低了 的权重，并将更多权重分配给异常交易量的注意力代理 ()，它提供了补充信息。最后，我们的 DMA-CF 方法可以提高碳期货投资组合的经济收益。