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.
Improving the expected accuracy of forecasts of future climate using a simple bias-variance tradeoff
We describe a simple method that utilises the standard idea of bias-variance trade-off to improve the expected accuracy of numerical model forecasts of future climate. The method can be thought of as an optimal multi-model combination between the forecast from a numerical model multi-model ensemble, on one hand, and a simple statistical forecast, on the other. We apply the method to predictions for UK temperature and precipitation for the period 2010 to 2100. The temperature predictions hardly change, while the precipitation predictions show large changes.
Weather Derivative Pricing and the Interpretation of Linear Trend Models
We interpret the flat line, best fit linear trend and damped linear trend models as a sequence of weights on historical index values. This gives further insight into some of the properties of these models.