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

Eduardo Mendes, Les Oxley and Marco Reale

Working Papers in Economics from University of Canterbury, Department of Economics and Finance

Abstract: 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.

Keywords: Electricty Time Series; Forecasting Performance; Semi- and Non- Parametric Methods (search for similar items in EconPapers)
JEL-codes: C14 C45 C53 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2008-01-01
New Economics Papers: this item is included in nep-ene and nep-for
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Citations: View citations in EconPapers (2)

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