Work

Do global COVOL and geopolitical risks affect clean energy prices? Evidence from explainable artificial intelligence models

Year: 2024

Type: article

Abstract: We investigate the impact of global common volatility and geopolitical risks on clean energy prices. Our study utilizes daily data from January 1, 2001, to March 18, 2024. Using a new framework based on explainable artificial intelligence (XAI) methods, our findings demonstrate that the COVOL index outperforms the geopolitical risk index in accurately predicting clean energy prices. Furthermore, the Extreme Trees algorithm shows superior performance compared to traditional regression techniques. Our findings indicate that XAI improves transparency, thereby making a substantial contribution to agile decision-making in predicting clean energy prices. Practitioners, including investors and portfolio managers, can enhance investment decisions and manage systemic risks by incorporating COVOL into their risk assessment and asset allocation models.

Source: Energy Economics

DOI: 10.1016/j.eneco.2024.108112

Authors: Sami Ben Jabeur, Yassine Bakkar, Oguzhan Cepni

Institution: Copenhagen Business School

Language: English

Topic: Market Dynamics and Volatility

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Climate action, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,680