In many electricity markets, retailers purchase electricity at an unregulated spot price and sell to consumers at a heavily-regulated price. Consequently the occurrence of extreme movements in the spot price represent a major source of risk to retailers and the accurate forecasting of these extreme events or price spikes is an important aspect of effective risk management. Traditional approaches to modeling electricity prices are aimed primarily at predicting the trajectory of spot prices. By contrast, this paper focus exclusively on the prediction of extreme events or spikes in electricity prices. The time course of price spikes is treated as a realization of a discrete-time point process. A nonlinear variant of the autoregressive conditional hazard model is used to analyze variables thought to drive this process. The forecasting performance of the model is evaluated against a benchmark that is consistent with the assumptions of commonly-used electricity pricing models. Applications to the electricity futures market are also discussed.
