An event risk premium appears when traders believe an event carries additional uncertainty—such as unclear data sources, unpredictable external factors, or potential last-minute surprises. Instead of pricing only the base probability, markets include an extra margin to account for these risks. This premium can push probabilities slightly higher or lower than the “pure” forecast would otherwise suggest.
On prediction platforms like Polymarket, Kalshi, Myriad, and Manifold, event risk premiums show up in markets tied to sensitive, complex, or highly volatile topics—such as economic releases, regulatory decisions, or rapidly evolving news. Traders may demand better odds to hold positions in such markets, especially when they expect sudden information shocks or resolution complications. These adjustments become visible in prediction markets data as sustained deviations from fundamental expectations.
Over time, understanding event risk premiums helps forecasters interpret why probabilities don’t always match simple statistical estimates. They reveal how markets internalize fear, uncertainty, and last-minute variability.
Event risk premiums help explain pricing patterns that aren’t driven purely by outcome likelihood. Recognizing these premiums improves interpretation of prediction markets data and clarifies when probabilities reflect uncertainty rather than true belief.
They appear when traders anticipate that an event may involve volatility, ambiguous information, or unexpected developments. Instead of treating all outcomes as equally predictable, markets embed a premium for uncertainty. This ensures traders are compensated for taking on risk in situations where outcomes are harder to interpret. As a result, prediction markets data reflects both event likelihood and perceived instability.
Risk premiums can skew probabilities away from expected values. For example, traders may push prices lower in events where late-breaking news could cause sharp reversals, or higher when they fear hidden downside risk. These premiums shape how probabilities evolve and often explain pricing that diverges from traditional models. Analysts studying prediction markets data must account for this extra layer of uncertainty.
Analysts can identify which markets consistently price in extra uncertainty, revealing event categories that traders perceive as especially risky. They can track how premiums change as events approach, signaling shifts in confidence or volatility expectations. These patterns help improve forecasting methods and provide a more nuanced read of prediction markets data.
On Kalshi, markets tied to government economic releases often show an event risk premium in the hours before publication. Traders anticipate potential surprises or revisions, so prices may drift or widen even if underlying expectations haven’t changed—capturing the market’s fear of last-minute volatility.
Understanding event risk premiums requires detailed, time-stamped probability and price data. FinFeed's Prediction Markets API provides structured prediction markets data that helps developers quantify premiums, analyze volatility patterns, and model how uncertainty affects pricing across different event types.
