Forecast volatility captures how much a prediction market’s probability moves in response to new information, uncertainty, or shifting sentiment. High volatility means the market is reacting sharply—often because traders face unclear signals, breaking news, or competing interpretations of the event. Low volatility indicates stability, where traders largely agree on the likelihood and little new information is changing expectations.
Markets on Polymarket, Kalshi, Myriad, and Manifold often show distinct volatility patterns depending on liquidity, event type, and timing. Major announcements, data releases, or sudden developments can cause rapid swings, while longer-term events may move gradually until key moments approach. These fluctuations become visible in prediction markets data as jagged or smooth probability curves.
Understanding forecast volatility helps reveal how information flows through the market, how confident traders are, and which events carry the most uncertainty.
Forecast volatility highlights how prediction markets respond to uncertainty and new information. It helps analysts interpret confidence levels, detect instability, and understand the reliability of prediction markets data.
It occurs when traders face shifting information, ambiguous signals, or unexpected developments. Strong news can drive sharp moves as informed traders adjust positions quickly. Low-liquidity markets can also amplify volatility because even small trades can move prices significantly. These conditions create prediction markets data that reflects both information shocks and market structure.
High volatility suggests that traders are uncertain or still processing new information. It may indicate disagreement within the crowd or a rapidly evolving situation. Low volatility signals that expectations are stable and consensus is strong. Analysts reviewing prediction markets data use volatility patterns to understand when forecasts are reliable and when markets may be struggling with noise or limited liquidity.
Analysts can identify which events trigger large swings, how quickly markets absorb news, and whether volatility clusters around specific time windows—such as announcements or deadlines. Volatility analysis also reveals liquidity conditions and the presence of informed traders. These insights make prediction markets data more interpretable and help improve forecasting models.
On Polymarket, a market predicting the outcome of a high-profile tech regulation vote showed intense forecast volatility as lawmakers released conflicting signals throughout the day. Each new update triggered rapid shifts in the probability as traders reassessed the likelihood of passage.
Tracking forecast volatility requires granular, time-stamped probability data. FinFeed's Prediction Markets API provides detailed prediction markets data that allows developers to measure volatility, detect unstable markets, and build tools that visualize how forecasts react to incoming information.
