Work

Carbon Convenience Yields and Probability Density Forecasts for Carbon Returns

Year: 2026

Type: article

Abstract: 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.

Source: Mathematics

DOI: 10.3390/math14020315

Authors: Meng Han, Jia You, Min Lin

Institution: Beijing Normal University, Zhuhai

Language: English

Topic: Financial Markets and Investment Strategies

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,137