Forecasting Electricity Price Spikes Using Support Vector Machines

Electricity markets are considered to be, the most volatile amongst commodity markets. The non-storability of electricity and the need for instantaneous balancing of demand and supply can often cause extreme short-lived fluctuations in electricity prices. These fluctuations are termed price spikes. In this paper, we employ a multiclass Support Vector Machine (SVM) model to forecast the occurrence of price spikes in the German electricity market. As price spikes, we define the prices that lie above the 95th quantile estimated by fitting a Generalized Pareto (GP) distribution in the innovation distribution of an AR-EGARCH model