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Some New Approaches to Forecasting the Price of Electricity: A Study of Californian Market

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In this paper we consider the forecasting performance of a range of semi- and non- parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and time- and price-dependent volatility. The typical approach in this area has been to use a range of tools that have proven popular in the financial econometrics literature, where volatility clustering is common. However, electricity time series tend to exhibit higher volatility on a daily basis, but within a mean reverting framework, albeit with occasional large ’spikes’. In this paper we compare the existing forecasting performance of some popular parametric methods, notably GARCH AR-MAX, with approaches that are new to this area of applied econometrics, in particular, Artificial Neural Networks (ANN); Linear Regression Trees, Local Regressions and Generalised Additive Models. Section 2 presents the properties and definitions of the models to be compared and Section 3 the characteristics of the data used which in this case are spot electricity prices from the Californian market 07/1999-12/2000. This period includes the ’crisis’ months of May-August 2000 where extreme volatility was observed. Section 4 presents the results and ranking of methods on the basis of forecasting performance. Section 5 concludes.

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Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 08/05.

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Length: 34 pages
Date of creation: 01 Jan 2008
Handle: RePEc:cbt:econwp:08/05
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