Forecasting carbon futures returns using feature selection and Markov chain with sample distribution

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.