Carbon prices forecasting in quantiles

Xiaohang Ren, Kun Duan, Lizhu Tao, Yukun Shi and Cheng Yan

Energy Economics, 2022, vol. 108, issue C

Abstract: 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.

Keywords: Carbon return predictability; Dimension reduction techniques; Out-of-sample forecasting; Quantile regression; LASSO penalty; SCAD penalty; Variable selection (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 Q01 Q43 Q47 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:108:y:2022:i:c:s0140988322000457

DOI: 10.1016/j.eneco.2022.105862

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