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.

中文翻译：

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全球 COVOL 和地缘政治风险会影响清洁能源价格吗？来自可解释的人工智能模型的证据


我们研究了全球常见波动性和地缘政治风险对清洁能源价格的影响。我们的研究利用了 2001 年 1 月 1 日至 2024 年 3 月 18 日的每日数据。使用基于可解释人工智能 （XAI） 方法的新框架，我们的研究结果表明，COVOL 指数在准确预测清洁能源价格方面优于地缘政治风险指数。此外，与传统回归技术相比，Extreme Trees 算法表现出卓越的性能。我们的研究结果表明，XAI 提高了透明度，从而为预测清洁能源价格的敏捷决策做出了重大贡献。包括投资者和投资组合经理在内的从业者可以通过将 COVOL 纳入其风险评估和资产配置模型来加强投资决策和管理系统性风险。