We analyze over 1.1 million news articles from The Wall Street Journal to identify the exogenous factors influencing stock market volatility. Using a two-step topic modeling and term frequency approach, we find that news narratives explain a substantial part of the stock market's time variation in volatility. Moreover, incorporating news-based measures into volatility models yields superior out-of-sample forecasts, reducing forecast errors by over 40% at the monthly horizon relative to benchmarks. These improvements translate into significant economic gains through increased realized utility and Sharpe ratios in volatility-targeted strategies. Our approach outperforms ChatGPT-derived predictors and benchmark economic indicators in volatility forecasts.
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