The accurate forecasting of carbon returns is paramount for enabling informed investment decisions, promoting emissions reduction, and effectively shaping policies to combat climate change. In this paper, we propose a novel method to improve carbon returns predictability in a data-rich environment. The innovations of the model are manifested in two key dimensions: (i) a feature selection strategy based on the minimum prediction error is introduced; (ii) a novel Markov chain with sample distribution considering both prediction and parameter estimation is proposed to quantify the error information and perfect the prediction performance by error modification. Our empirical findings demonstrate that the proposed model outperforms a comprehensive array of competing models, both in point and interval forecasting of carbon returns. The results are consistently confirmed in various robustness checks. Finally, we show that the enhanced prediction performance of the proposed model is economically significant, which can help investors make favorable decisions.

中文翻译：

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使用特征选择和具有样本分布的马尔可夫链预测碳期货回报


准确预测碳回报对于做出明智的投资决策、促进减排和有效制定应对气候变化的政策至关重要。在本文中，我们提出了一种在数据丰富的环境中提高碳回报可预测性的新方法。该模型的创新体现在两个关键维度上：（i） 引入了基于最小预测误差的特征选择策略;（ii） 提出了一种同时考虑预测和参数估计的样本分布的新型马尔可夫链，以量化误差信息并通过误差修正来完善预测性能。我们的实证结果表明，所提出的模型在碳回报的点和区间预测方面都优于一系列全面的竞争模型。结果在各种稳健性检查中得到一致确认。最后，我们表明所提出的模型的增强预测性能具有经济意义，可以帮助投资者做出有利的决策。