Published methods for the pricing of weather derivatives are based on classical statistics, in that the predictions of the distributions of weather indices that they use are based on best estimates of model parameters. It is likely that such methods do not accurately capture the true uncertainty...

We derive closed-form expressions for the expected payoff of weather derivatives contracts for a t distributed weather index. There are three common situations in which t distributions might serve as a reasonable model for weather indices: first, some weather variables may be t distributed; second...

Many common weather indices are very close to being normally distributed, and it may be reasonable to assume they are exactly normally distributed for the purpose of pricing weather derivatives. Given that assumption, how should the indices be modelled? We use the expected out-of-sample...

The normal distribution is commonly used to predict weather indices when pricing weather derivatives. The standard method for making such predictions involves calculating an unbiased estimator for the population variance. The variance of the prediction (the predictive variance) is then the...

Weather derivative pricing for US locations can potentially be improved through the judicious use of seasonal forecasts. However, the number of El Nino and La Nina events that appear in the historical record is small and for many locations the signals are weak. We run some simulation-based tests...

Seasonal forecasts of US temperature have the potential to improve weather derivative pricing. One way to produce such forecasts is to divide the range of possible Pacific ocean surface temperatures into three categories and look at the US temperatures that occur for each category. Unfortunately...

It is often desirable to remove the trends from historical meteorological data prior to using that data for the pricing of weather derivatives. In previous articles we have introduced the method of damped linear detrending and argued that it is an effective way to remove trends when the trends...

The actuarial pricing of weather swaps often depends on predicting the temperature several months in advance. Such predictions can be made by modelling the trends in historical temperature indices and extrapolating them forward in time. Linear trends are one of the simplest types of trend model...

The pricing of temperature derivatives is often based on statistical methods that predict the surface air temperature a year in advance. We perform an empirical comparison of three simple methods for such year-ahead temperature forecasting and draw clear conclusions about their relative merits....

One of the most important decisions to make in the actuarial pricing of weather derivatives is how to detrend the historical meteorological data record. We derive expressions for the statistical behaviour of an automatic decision rule that chooses between two of the most commonly used models:...