Gordian Knots

Via Infectious Greed:

There is a nifty new paper (in PDF format) that describes how you can fruitfully use simple time series U.S. weather data to make in money in weather markets:

We take a simple time-series approach to modeling and forecasting
daily average temperature in U.S. cities, and we inquire systematically
as to whether it may prove useful from the vantage point of
participants in the weather derivatives market. The answer is, perhaps
surprisingly, yes. Time-series modeling reveals conditional mean
dynamics, and crucially, strong conditional variance dynamics, in daily
average temperature, and it reveals sharp differences between the
distribution of temperature and the distribution of temperature
surprises. As we argue, it also holds promise for producing the
long-horizon predictive densities crucial for pricing weather
derivatives, so that additional inquiry into time-series weather
forecasting methods will likely prove useful in weather derivatives
contexts.