Interest in nuclear energy has increased recently due to its low-carbon footprint, energy security concerns, and technological advances. Despite the recent surge in uranium stocks, there is a lack of research on uranium sector volatility. We fill this gap by analyzing the volatility of the Global X Uranium ETF (URA) from 2010 to 2024 using high-frequency data. Our analysis reveals that HAR models effectively capture URA volatility. Market-wide implied volatility and investor attention, captured by Google search volume, are found to contain valuable information for forecasting uranium sector volatility in an in-sample context. In contrast, economic and geopolitical uncertainty, as well as global financial risk, exhibit limited relevance. Although advanced models show some improvement in out-of-sample predictions, the basic HAR model remains a robust benchmark.

中文翻译：

---

是什么推动了铀行业的风险？注意力的作用、经济和地缘政治不确定性


由于核能的低碳足迹、能源安全问题和技术进步，最近人们对核能的兴趣有所增加。尽管最近铀库存激增，但缺乏对铀行业波动性的研究。我们通过使用高频数据分析 Global X 铀 ETF （URA） 从 2010 年到 2024 年的波动性来填补这一空白。我们的分析表明，HAR 模型有效地捕捉了 URA 的波动性。Google 搜索量捕获的整个市场的隐含波动率和投资者关注度被发现包含了在样本内环境中预测铀行业波动率的宝贵信息。相比之下，经济和地缘政治不确定性以及全球金融风险的相关性有限。尽管高级模型在样本外预测方面显示出一些改进，但基本 HAR 模型仍然是一个可靠的基准。