Predicting European carbon emission price movements

• Published in: Carbon management Vol. 8; no. 1; pp. 33 - 44
• Main Authors: Hong, KiHoon, Jung, Hojin, Park, Minjae
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 02.01.2017; Taylor & Francis Group
• Subjects: autoregressive; data mining; European carbon emission; return prediction
• ISSN: 1758-3004; 1758-3012
• DOI: 10.1080/17583004.2016.1275813

The European carbon emission trading market is critical in achieving planned carbon emission reduction for global sustainable growth. This paper investigates various statistical methods in forecasting the European carbon emission (CO 2 hereafter) price movements. The paper builds a predictive regression model of CO 2 price movements with past returns of various commodities and financial products. In the paper, 22 functional forms of five different classifiers are employed and CO 2 price movements are forecast. Results indicate that the past returns of Brent crude futures, natural gas (NG), Financial Times Stock Exchange 100 (FTSE100), Deutscher Aktienindex (German stock index) 30 (DAX30), Cotation Assistée en Continu (French stock index) 40 (CAC40) and Standard & Poor's 500 (S&P500) are statistically significant in forecasting the current CO 2 price movements. The authors also found that the bagged decision tree of the ensemble classifier best forecasts the CO 2 price movements. The result should be relevant to firms that wish to trade European carbon emissions.