Carbon Convenience Yields and Probability Density Forecasts for Carbon Returns

We explore the role of carbon convenience yields in forecasting the probability density of carbon returns. While theory suggests that convenience yields contain forward-looking information, their predictive content for carbon returns—especially in a density forecasting framework—remains underexplored. We propose a probability density forecasting approach that combines a mixed data sampling (MIDAS) regression with a non-parametric bootstrap and kernel density estimation. Using data from the European carbon market, we find that convenience yields significantly predict carbon returns. It takes approximately 19 days for a disturbance in carbon convenience yields to affect carbon returns, with the impact persisting for around 27 days. Moreover, our approach outperforms existing benchmark models in predicting the probability density of carbon returns, showing superior predictive accuracy and robustness.