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Carbon Prices Forecasting Using Group Information | Published in Energy RESEARCH LETTERS

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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. The issue of greenhouse gas emissions is now receiving widespread global attention. According to estimates from relevant studies, the harm caused by each ton of CO2 is worth approximately US$50 (Revesz et al., 2017). In order to alleviate the worsening environmental problems, the Kyoto Protocol, established in Kyoto, Japan, in 1997, brought the carbon emission rights market to reality. As a derivative market of the carbon spot market, the carbon futures market theoretically has the function of price f

Carbon Prices Forecasting Using Group Information | Published in Energy RESEARCH LETTERS Loading [MathJax]/jax/element/mml/optable/GeneralPunctuation.js This website uses cookies We use cookies to enhance your experience and support COUNTER Metrics for transparent reporting of readership statistics. Cookie data is not sold to third parties or used for marketing purposes. Deny cookies Customize ≫ cookies Allow all cookies Skip to main content Energy RESEARCH LETTERS RSS Feed Enter the URL below into your favorite RSS reader. http://localhost:46423/feed × P-ISSN 2652-6514 E-ISSN 2652-6433 Peer-r

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