Modeling Volatility in Prediction Markets

Nowadays, there is a significant experimental evidence that prediction markets are efficient mechanisms for aggregating information and are more accurate in forecasting events than traditional forecasting methods, such as polls. Interpretation of prediction market prices as probabilities has been exten-sively studied in the literature. However there is very little research on the volatility of prediction market prices. Given that volatility is fundamental in estimating the significance of price movements, it is important to have a better under-standing of volatility of the contract prices. In this paper, we present a model of a prediction market with a binary payoff on a competitive event involving two par-ties. In our model, each party has some underlying “ability” process that describes its ability to win and evolves as an Ito diffusion, a generalized form of a Brownian motion. We show that, if the prediction market for this event is efficient and unbiased, the price of the corresponding contract also follows a diffusion and its instantaneous volatility is a function of the current contract price and its time to expiration. In the ex-perimental section, we validate our model on a set of InTrade prediction markets and show that it is consistent with ob-served volatility of contract returns and outperforms existing volatility models in predicting future contract volatility from historical price data. To demonstrate the practical value of our model, we apply it to pricing options on prediction mar-ket contracts, such as those recently introduced by InTrade. Other potential applications of this model include detection of significant market moves and improving forecast standard errors. 1