Research Repository

Carbon Prices Forecasting in Quantiles

Ren, Xiaohang and Duan, Kun and Tao, Lizhu and Shi, Yukun and Yan, Cheng (2022) 'Carbon Prices Forecasting in Quantiles.' Energy Economics, 108. p. 105862. ISSN 0140-9883

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This paper proposes two new methods (the Quantile Group LASSO and the Quantile Group SCAD models) to evaluate the predictability of a large group of factors on carbon futures returns. The most powerful predictors are selected through the dimension- reduction mechanism of the two models, while potential differences of the statistically significant predictors for different quantiles of carbon returns are carefully considered. First, we find that the proposed models outperform a series of competing ones with respect to prediction accuracy. Second, impacts of the selected predictors over the carbon price distribution are estimated through a quantile approach, which outperforms the mean shrinkage model in our case with data featured by a non-normal distribution. Specifically, the Brent spot price, the crude oil closing stock in the UK, and the growth of natural gas production in the UK are found to impact carbon futures returns only in extreme conditions with a strong asymmetric feature. Importantly, our estimators remain robust against the extreme event caused by the Covid-19. Our findings reveal that the identification of appropriate carbon return predictors and their impacts hinge on the carbon market conditions, and should be of interest to various stakeholders.

Item Type: Article
Uncontrolled Keywords: Carbon return predictability; Dimension reduction techniques; Out-of-sample forecasting; Quantile regression; LASSO penalty; SCAD penalty; Variable selection
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 26 Jan 2022 11:44
Last Modified: 12 Aug 2022 16:34

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