Excited to share our new paper with @malleshpai @ciamac @Qiaoqiao2001 !
Two prediction markets can have the same price, yet very different uncertainty about where that price may move next. We develop a structural approach to quantifying and forecasting that uncertainty.
The paper is now on arXiv and I also built an interactive explorer where you can search across Kalshi markets and trace how the fitted uncertainty bands evolve over time: pm-vol.pages.dev
Mallesh Pai@malleshpai
🚨New Paper with @Weiye_Xi , @ciamac and @Qiaoqiao2001 Here’s two different prediction markets priced at ~6.5%: 1. Will the US confirm the existence of Aliens in 2026? 2. *that* Spurs @ Knicks Game 4, ~4th quarter. They suggest that both these events are the same probability, but intuitively these feel very different: the latter (Knicks won!) feels a lot more uncertain. But prediction markets don’t immediately give us a way to quantify it.
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