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Structural Volatility Forecasting for Binary Prediction Markets

Article arXiv papers · Author: Weiye Xi et al.
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Summary

The document develops a volatility forecasting framework for binary prediction markets, where prices represent bounded probabilities and contracts resolve at known deadlines. It combines a Wright–Fisher component to model how uncertainty is forced to resolve as deadlines approach with a Glosten–Milgrom component that links informed order flow, spreads, and volume to volatility. It also compares these structural inputs with ARCH and GARCH benchmarks and considers adding residual GARCH dynamics.

Tests on a large panel of Kalshi contracts find that the structural variables improve forecasts over plain ARCH/GARCH models, while combining them with residual GARCH dynamics gives the strongest overall forecasts. The framework attributes higher volatility to prices near an even probability and to contracts nearing resolution. It also reports differences between smoother economics contracts and more event-concentrated sports contracts. Category-specific fitting does not consistently improve out-of-sample results, so these findings support transfer across the studied categories but do not establish performance beyond the available contracts.

Key ideas

  • Binary contract volatility reflects both deadline-driven resolution and informed trading.
  • The model uses probability levels and time to resolution as structural forecasting inputs.
  • Structural specifications outperform plain ARCH/GARCH benchmarks in the reported panel.
  • Adding residual GARCH dynamics yields the best overall forecasts in the study.
  • Volatility patterns vary across contract categories, but category-specific fits do not consistently help out of sample.

Tags

VolatilityDerivatives pricingMarket microstructureStatisticsRisk management
Full text
# Volatility in Prediction Markets: A Structural Approach


# Volatility in Prediction Markets: A Structural Approach









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.