Template-Type: ReDIF-Article 1.0 
Author-Name: Xiaohang Ren
Author-Name:  Kang Yuan
Author-Name:  Lizhu Tao
Author-Name:  Cheng Yan
Author-Email:  lizhutao@scu.edu.cn
Author-Workplace-Name:  School of Business, Central South University, China
Title: Carbon Prices Forecasting Using Group Information
Abstract:  We select 44 macroeconomic variables as predictors and employ multiple statistical models to forecast EU carbon futures price returns. The predictors in this study are high-dimensional and have the group structure, and we find that, in this case, the accuracy of the high-dimensional models for forecasting carbon prices are higher than traditional time series models. In addition, the introduction of group structure variables into the high-dimensional model improves forecasting performance.
Classification-JEL:  C52 ,C53 ,Q43
Keywords:  Carbon return predictability, High-dimensional models, Group structure
Journal:  Energy RESEARCH LETTERS
Pages: 1-6
Volume: 4
Issue: 4
Year:  2024
DOI:  2024/07/09
File-URL:  https://erl.scholasticahq.com/api/v1/articles/36615-carbon-prices-forecasting-using-group-information.pdf
File-Format:  Application/pdf
Handle:  RePEc:ayb:jrnerl:92