Do global COVOL and geopolitical risks affect clean energy prices? Evidence from explainable artificial intelligence models
利用2001年至2024年的日度数据，通过可解释人工智能方法发现全球共同波动性指数比地缘政治风险指数更能准确预测清洁能源价格，为投资者优化决策提供依据。
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. • Analysis of the effects of COVOL and geopolitical risks on clean energy prices. • The COVOL index provides superior predictive accuracy for clean energy prices. • XAI enhances transparency in forecasting clean energy prices. • Investors can leverage the COVOL index to improve investment decisions.