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Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

  • Misiorek Adam


    (Institute of Power Systems Automation)

  • Trueck Stefan


    (Queensland University of Technology)

  • Weron Rafal


    (Wroclaw University of Technology)

In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX (''X'' stands for exogenous/fundamental variable -– system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-of-sample point and interval forecasting in normal and extremely volatile periods preceding the market crash in winter 2000/2001. We find evidence that (i) non-linear, threshold regime-switching (TAR/TARX) models outperform their linear counterparts, both in point and interval forecasting, and that (ii) an additional GARCH component generally decreases point forecasting efficiency. Interestingly, the former result challenges a number of previously published studies on the failure of non-linear regime-switching models in forecasting.

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Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 10 (2006)
Issue (Month): 3 (September)
Pages: 1-36

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Handle: RePEc:bpj:sndecm:v:10:y:2006:i:3:n:2
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