Expected volatility data in prediction markets focuses on how much price movement traders anticipate because of an upcoming event. Instead of looking at random fluctuations, it captures volatility that is directly linked to scheduled announcements, policy decisions, economic releases, hearings, or other time-bound developments. As the event nears, traders adjust positions in anticipation of new information—creating measurable volatility expectations.
Platforms like Polymarket, Kalshi, Myriad, and Manifold often show rising expected volatility before major events. Markets become more sensitive to signals, and probabilities may swing more quickly as participants position themselves. This behavior appears clearly in prediction markets data as widening movements, increased trading, and more reactive probability curves. Expected volatility is especially notable in markets tied to economic indicators, regulatory actions, political decisions, and industry announcements.
By understanding expected volatility, analysts can anticipate when a market is likely to become more active, more reactive, or more uncertain as an event approaches.
Expected volatility data helps analysts understand how unstable a forecast may become during event-driven periods. It strengthens interpretation of prediction markets data by revealing when sharp movements are likely and why they occur.
As a scheduled event approaches, traders expect new information—sometimes decisive information—to arrive. This anticipation makes participants reposition, hedge, or double down. Even before news is released, the expectation of change increases volatility. These patterns appear consistently in prediction markets data, especially for markets tied to datelines or official releases.
Expected volatility data helps analysts distinguish normal market noise from meaningful, event-driven movement. When volatility rises because an event is imminent, it signals growing uncertainty or heightened sensitivity to new developments. This makes prediction markets data easier to interpret and helps forecasters prepare for sudden shifts.
Analysts can identify which events trigger the strongest market reactions, how early volatility begins to build, and how sharply expectations move once the event occurs. They can also measure how well markets absorb new information after high-volatility moments. These insights make prediction markets data more valuable for modeling and scenario analysis.
On Kalshi, markets tied to monthly inflation numbers show rising expected volatility as the release date approaches. Traders anticipate sharp probability adjustments once the official figure is announced, causing increased trading activity and wider movements in the days leading up to the event.
Event-driven volatility analysis requires high-resolution probability and liquidity data. FinFeed's Prediction Markets API provides structured prediction markets data that developers can use to model expected volatility, build alerts, and forecast market reactions to upcoming events.
