Carbon Price Forecasting: Which Driven Factors Can Help

Abstract

This paper proposes two dimension-reduction and forecasting quantile methods (i.e., the quantile group lasso and the quantile group SCAD models) to predict carbon futures returns and investigate the predictability of a comprehensive group of factors including market fundamental variables and technical variables. In terms of the predictive performance, the two proposed models outperform a series of popular competing models. In terms of robustness, the employed quantile method outperforms the mean shrinkage models, especially in the case of our empirical dataset with a non-normal distribution. Through the predictor selection process, the most powerful predictors of carbon futures returns are selected through the dimension-reduction mechanism of the two employed models, while the possible difference of the selected predictors for different quantiles of carbon returns are carefully considered. Moreover, the impacts of those selected predictors are eventually estimated on the quantiles of carbon returns through a quantile regression. We find that the Brent price is the key factor of carbon returns at different quantile levels. The crude oil closing stock and the natural gas futures in the UK significantly influence the carbon futures returns at lower and higher quantile levels. Our research shows that appropriate predictors for carbon futures returns and their impacts hinge on carbon market conditions.