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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27716
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| Title: | Modeling Volatility in Prediction Markets |
| Authors: | Archak, Nikolay
Ipeirotis, Panagiotis G. |
| Keywords: | prediction market
stochastic model applications
volatility |
| Issue Date: | 1-Oct-2008 |
| Series/Report no.: | CeDER-08-07 |
| Abstract: | Nowadays, there is a significant experimental evidence of excellent
ex-post predictive accuracy in certain types of prediction markets, such
as markets for elections. This evidence shows 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
little attention so far has been given to understanding volatility of
prediction market prices. In this paper, we present a model of a
prediction market with a binary payoff on a competitive event involving
two parties. In our model, each party has some 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 accurate, the price of the corresponding contract will
also follow a diffusion and its instantaneous volatility is a particular
function of the current claim price and its time to expiration. We
generalize our results to competitive events involving more than two
parties and show that volatilities of prediction market contracts for
such events are again functions of the current claim prices and the time
to expiration, as well as of several additional parameters (ternary
correlations of the underlying Brownian motions). In the experimental
section, we validate our model on a set of InTrade prediction markets
and show that it is consistent with observed volatilities of contract
returns and outperforms the well-known GARCH model 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. |
| URI: | http://hdl.handle.net/2451/27716 |
| Appears in Collections: | CeDER Working Papers
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