NDLI: Modeling volatility in prediction markets

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Modeling volatility in prediction markets

Content Provider	ACM Digital Library
Author	Archak, Nikolay Ipeirotis, Panagiotis G.
Abstract	There is 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 extensively studied in the literature. However there is little research on the volatility of prediction market prices. Given that volatility is fundamental in estimating significance of price movements, it is important to have a better understanding of the volatility of the contract prices. This paper presents a model of a prediction market with binary payoff on a competitive event involving two parties. In our model, each party has a latent underlying "ability" process that describes its ability to win and evolves as an Ito diffusion. 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 experimental section, we validate our model on a set of InTrade prediction markets and show that our model is consistent with the observed volatility of contract returns. Our model also 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 market 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.
Starting Page	275
Ending Page	284
Page Count	10
File Format	PDF
ISBN	9781605584584
DOI	10.1145/1566374.1566414
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-07-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Binary option Volatility Forecasting Prediction market
Content Type	Text
Resource Type	Article

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