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Weiye Xi, Ciamac C. Moallemi, Mallesh Pai, Shouqiao Want · 2026-07-09
The paper builds a volatility forecasting model designed specifically for binary prediction markets, where prices are bounded probabilities that resolve to yes/no at a known deadline. It combines two structural mechanisms—one capturing how binary uncertainty must resolve as the deadline approaches, and one capturing volatility from informed trading via spreads and volume—and tests it on a large panel of Kalshi contracts. The structural variables carry substantial forecasting power and beat plain ARCH/GARCH benchmarks, with the best results coming from combining the structural model with residual GARCH dynamics.
Why it matters: For anyone market-making or trading volatility-linked positions in prediction markets, this suggests standard GARCH tools are poorly suited and that structural features (proximity to 50/50, time to resolution, spreads/volume) forecast volatility better. The interpretable framework could help with pricing, risk management, and anticipating when volatility spikes—though it is an academic finding, not a trading edge guarantee.
⚠ Results are from historical Kalshi data and forecasting comparisons, not live trading, and apply specifically to binary prediction markets rather than standard asset classes.
Forward-looking volatility forecasts are central inputs to derivatives pricing, market making, risk management, and volatility-linked trading strategies, with ARCH and GARCH models serving as the canonical workhorses. Such models are natural in standard asset markets, where prices are positive-valued stochastic processes and volatility is typically inferred from return dynamics. Prediction markets have a different structure: prices are bounded probabilities, payoffs are binary, and contracts resolve at known deadlines. We develop and estimate a volatility model tailored to binary prediction markets. The model combines two economic mechanisms: a Wright-Fisher deadline-resolution component, capturing how remaining binary uncertainty is forced to resolve over time, and a Glosten-Milgrom order-flow component, capturing volatility from informed trading as reflected in spreads and volume. Using a large panel of Kalshi contracts, we show that these structural variables carry substantial forecasting power. Plain ARCH/GARCH benchmarks are dominated by structural specifications; combining the structural model with residual GARCH dynamics gives the best overall forecasts. The model also provides an interpretable measurement framework: volatility is highest near fifty-fifty prices, rises near resolution, and varies across categories with the timing and discreteness of information arrival. Economics contracts are closer to smooth deadline-resolution dynamics, while sports contracts exhibit more event-concentrated, jump-like behavior. Across major categories, category-specific fitting does not systematically improve out-of-sample performance, suggesting that the structural specification transfers beyond the pooled headline result.
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AI summary generated from the paper’s public abstract via arXiv; it may miss nuance — read the source before relying on it. Thank you to arXiv for its open-access interoperability; StockTools is not affiliated with arXiv, and all rights remain with the authors. Educational only, not financial advice.