Weather Derivative Pricing and the Year-Ahead Forecasting of Surface Air Temperature: An Empirical Evaluation of Damped Linear Detrending
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. The three methods we consider are the standard flat-line and best-fit linear detrending methods and the recently introduced damped linear detrending method.
Closed Form Expressions for the Uncertainty from Linear Detrending, and the Pricing of Weather Derivatives
Weather derivative pricing is often based on a statistical analysis of past weather data. Such data may be non-stationary, and detrending methods can be used to attempt to remove the non-stationarity in the mean. The performance of different methods can be compared by considering aspects of the distribution of errors in the predictions they make. In this paper we derive closed-form expressions for the mean error, error variance and mean square error for flat line and linear trend models in the case where the real trend is linear.
Weather Derivative Pricing and a Preliminary Investigation into a Decision Rule for Detrending
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 test a simple decision rule based on closed-form expressions for the RMSE.