I want to argue that prediction market contracts about future prices may be quite useful for planners, even in markets where a robust futures market already exists (though not where robust options markets exist).

Prediction market contracts about prices differ from futures and options mostly in their purpose. The purpose of prediction market contracts about prices is to reveal information whereas the purpose of futures and options markets to allow arbitrage and hedging. Because the purpose of prediction market contracts is to reveal information, such markets will often exist in areas where futures and options markets do not exist, and they will frequently be subsidized.

The most general type of prediction market about prices is a price probability distribution market, which actually consists of number of different contracts. Each contract predicts the probability that the price of a certain good will fall in a certain range on a certain date. The rangers are continuous, so the prices of all the markets can be interpreted as a probability distribution for the price (as shown in the graph).

In markets where futures markets do not exist, such probability distributions are useful to planners because they capture all public knowledge and some private knowledge about future prices. Additionally, unlike expert predictions, market probabilities are generally well calibrated (events which are predicted to happen with a probability of p=.50 happen 50% of the time, and so on). This makes them much easier to rely upon than expert predictions which tend to be overconfident or underconfident. Markets also make it much easier to share information between people, even competitors.

Why might a future price distribution be useful even when there is already a futures market? The general answer is that many agents do not have utilities that are linear with the price of some goods. The futures price of a commodity should be it’s expectation (mean) price, but this not very useful for buyers who have elastic demand curves or suppliers who have elastic supply curves.

For example, consider a company considering investing in wind power in the future. They are considering buying marginal land for wind turbines which would be profitable in energy prices rise beyond a certain price. How much they should be willing to pay for such land depends on the probability that energy prices rise above that cutoff price and the expectation price given that it is above the cutoff. Thus, the land may be quite valuable, even when futures price of energy is below the cutoff but there is much uncertainty about the future price. In this case, a probability distribution over future prices is helpful because these both the probability and the expectation are easily determined from a price probability distribution (actually, I think most energy markets have active options prices from which you can get this type of information right now, so this may not be a great example).

Another example is a private bus company figuring out how big their buses should be in a city with congestion pricing. If the city’s congestion charges are very large in the future, the the company will compensate by using larger buses. Since the company’s profit is not constant with the price (the company can mitigate the impact of the price to some extent) it is useful for the company to know the probability distribution of future congestion charges.

I should have more to say about prediction market contracts about prices in the future.