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:...

Weather derivative pricing is often based on year-ahead forecasts of the weather made using simple statistical models and past weather data. Two of the simplest models in common use are the flat line and linear trend models. We consider the question of how to choose between these two models and...

We consider the question of how to derive a stochastic process for the expectation of a non-linear weather index. Under various reasonable assumptions we show that the stochastic process is a deterministic function of Brownian motion. This generalises earlier results which only applied to linear...