Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals

Abstract

Using a broad selection of 53 carbon (EUA) related, commodity and financial predictors, we provide a comprehensive assessment of the out-of-sample (OOS) predictability of weekly European carbon futures return. We assess forecast performance using both statistical and economic value metrics over an OOS period spanning from January 2013 to May 2018. Two main types of dimension reduction techniques are employed: (i) shrinkage of coefficient estimates and (ii) factor models. We find that: (1) these dimension reduction techniques can beat the benchmark significantly with positive gains in forecast accuracy, despite very few individual predictors being able to; (2) forecast accuracy is sensitive to the sample period, and only Group-average models and Commodity-predictors tend to beat the benchmark consistently; the Group-average models can improve both the prediction accuracy and stability significantly by averaging the predictions of All-predictors model and the benchmark. Further, to demonstrate the usefulness of forecasts to the end-user, we estimate the certainty equivalent gains (economic value) generated. Almost all dimension reduction techniques do well especially those which apply shrinkage alone. We find including All-predictors and Group-average variable sets achieve the highest economic gains and portfolio performance. Our main results are robust to alternative specifications.