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Please use this identifier to cite or link to this item: http://hdl.handle.net/10092/2069

Title: Some new approaches to forecasting the price of electricity: a study of Californian market
Authors: Mendes, E.F.
Oxley, L.
Reale, M.
Keywords: electricity time series
forecasting performance
semi- and non parametric methods
Issue Date: 2008
Citation: Mendes, E.F., Oxley, L., Reale, M. (2008) Some new approaches to forecasting the price of electricity: a study of Californian market. University of Canterbury. 32pp..
Source: http://www.econ.canterbury.ac.nz/research/pdf/0805.pdf
Abstract: In this paper we consider the forecasting performance of a range of semi- and nonparametric 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.
Publisher: Department of Economics
University of Canterbury. Economics.
University of Canterbury. Mathematics and Statistics.
Description: RePEc Working Paper Series: No. 05/2008
Research Fields: Fields of Research::340000 Economics::340400 Econometrics::340401 Economic models and forecasting
Fields of Research::340000 Economics::340400 Econometrics::340402 Econometric and statistical methods
URI: http://hdl.handle.net/10092/2069
Rights URI: http://library.canterbury.ac.nz/ir/rights.shtml
Appears in Collections:Engineering: Working Papers
Business and Law: Working Papers

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