Taylor, James and Buizza, Roberto (2006) Density Forecasting for Weather Derivative Pricing. International Journal of Forecasting, 22 (1). pp. 29-42.
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.
|Keywords:||Density forecasting, Weather risk management, Weather ensemble predictions, GARCH, Quantile regression, Quantile autoregression|
|Date Deposited:||05 Feb 2012 14:59|
|Last Modified:||10 Feb 2017 16:54|
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