🤖 AI Summary
Traditional ARCH/GARCH models struggle to capture the distinctive volatility dynamics in binary prediction markets, where prices represent bounded probabilities, exhibit a fixed expiration date, and yield binary payoffs. This work proposes the first volatility model that integrates economic structural mechanisms: it combines a Wright–Fisher process to characterize the convergence of uncertainty as contracts approach expiry and a Glosten–Milgrom order-flow mechanism to capture volatility induced by informed trading. The resulting structural framework not only yields interpretable volatility measures but also reveals fundamental differences in information arrival patterns between economic and sports-related contracts. Empirical analysis using large-scale Kalshi data demonstrates that the proposed structural variables significantly outperform standard GARCH specifications, with a hybrid structural-GARCH model achieving the best predictive performance and exhibiting strong cross-category generalization capabilities.
📝 Abstract
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.