Work

Disentangling and hedging global warming risk: A machine learning approach

Year: 2025

Type: article

Abstract: As global warming provokes increasing attention from investors, this study disentangles global warming risk (GWR) for investors by leveraging energy futures volatilities. This study derives GWR from energy futures using an extreme gradient boosting (XGB)-genetic programming (GP) framework. Our XGB-GP framework develops volatility forecasting models for GWR from selected energy futures markets identified by XGB as key contributors to global warming, surpassing traditional models in forecasting accuracy. The originality of the study rests on the pioneering integration of the XGB-GP framework in predicting climate risk, linking energy futures markets with climate risk management and enabling feasible climate-featured portfolio hedging. Our study also sheds new insights for policymakers to design carbon trading systems and carbon pricing mechanisms, as they can use relevant energy futures prices as a basis for carbon trading calibration.

Source: Environmental Impact Assessment Review

DOI: 10.1016/j.eiar.2025.107987

Authors: Shusheng Ding, Tianxiang Cui, Anna Min Du, John W. Goodell, Nanjiang Du

Institutions: Ningbo University, University of Nottingham Ningbo China, University of Akron

Language: English

Topic: Sustainable Finance and Green Bonds

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Climate action, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,900