کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
998311 | 1481478 | 2006 | 14 صفحه PDF | دانلود رایگان |
Weather derivatives enable energy companies to protect themselves against weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. They can be used to forecast the density of the payoff from a weather derivative. The mean of the density is the fair price of the derivative, and the distribution about the mean is important for risk management tools, such as value-at-risk models. In this empirical paper, we use 1- to 10-day-ahead temperature ensemble predictions to forecast the mean and quantiles of the density of the payoff from a 10-day heating degree day put option. The ensemble-based forecasts compare favourably with those based on a univariate time series GARCH model. Promising quantile forecasts are also produced using quantile autoregression to model the forecast error of an ensemble-based forecast for the expected payoff.
Journal: International Journal of Forecasting - Volume 22, Issue 1, January–March 2006, Pages 29–42