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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.