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This article uses public perceptions to forecast short-term fluctuations in asset prices. Based on four billion tweets scraped between 2009 and 2019, I perform textual analysis to construct daily sentiment indices. The sentiment indices allow us to forecast stock volatility jumps as well as expected jump levels. The implications of forecasting volatility jumps are substantive. First, volatility jumps have a significant effect on option prices. Second, changes in the volatility path lead to large (negatively related) changes in the prices’ future trajectory. Determining what information causes jumps allows for better risk management and more accurate asset pricing models.

Does Perception Matter in Asset Pricing? Modeling Volatility Jumps Using Twitter-Based Sentiment Indices