Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on the carbon price. Due to its volatility and non-linearity, predicting accurate carbon prices is generally a difficult task. In this study, we propose to improve carbon price forecasting with several novel practices. First, we collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. Then we select the most significant factors and disclose their optimal grouping for explainability. Finally, we use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions. We demonstrate through extensive experimental studies that our proposed methods outperform existing ones. Also, our quantile predictions provide a complete profile of future prices at different levels, which better describes the distributions of the carbon market.

翻译：碳期货近期已成为欧盟和中国等交易市场的新型金融资产。监测碳价趋势对国家政策制定及工业制造规划均至关重要。然而，地缘政治、社会及经济等多重因素会对碳价产生显著影响。由于碳价具有波动性和非线性特征，其精准预测通常是一项艰巨任务。本研究提出通过若干创新实践改进碳价预测：首先，我们收集包括商品价格、石油与天然气等出口量及景气指数在内的多种影响因子；其次，筛选出最具显著性的因子并揭示其最优分组以增强可解释性；最后，采用稀疏分位数群组套索和自适应稀疏分位数群组套索实现稳健的价格预测。通过大量实验研究证明，所提方法优于现有方法。此外，我们的分位数预测能够提供不同置信水平下未来价格的完整分布特征，从而更准确地刻画碳市场运行态势。