A recent paper on arXiv (q-fin.TR) proposes a structural approach to volatility in prediction markets. Unlike traditional financial markets where prices are positive-valued stochastic processes, prediction markets operate with probabilities bounded between 0 and 1, binary payoffs, and known deadlines. Standard ARCH/GARCH models, designed for continuous returns, do not apply directly.
The authors develop a volatility model specific to binary prediction markets, combining two economic mechanisms: - A Wright-Fisher deadline-resolution component, capturing how remaining binary uncertainty is forced to resolve over time. - A Glosten-Milgrom order-flow component, capturing volatility from informed trading.
This model allows estimation of implied volatility for prediction contracts, opening the door to hedging, market making, and variance trading strategies in these markets.
What does this mean for algorithmic trading?
At Strategy Arena, we have validated our Smart Money Evolved indicator across 15 assets after Monte Carlo cross-validation filtering. This signal, designed to detect informed flow imbalances, applies to both traditional and prediction markets, provided calibration is adapted to bound and deadline constraints.
The paper confirms that prediction market volatility is not residual noise but a structured, measurable, and forecastable quantity. For an algorithmic trader, this means it is possible to build market-specific volatility models and apply market making or directional trading strategies with fine-tuned calibration.
Caveat
This model has been tested on historical data and in backtesting. It has not been validated in live trading. Past performance does not guarantee future results. Prediction market volatility can be influenced by exogenous factors (news, political events) not captured by the model. We recommend always testing strategies in paper trading before any live deployment.
For more on our validation methodology: our methodology.
Source: arXiv:2607.08199
Strategy Arena metric: Smart Money Evolved cross-asset – validated across 15 assets after Monte Carlo CV filtering. View the signal.