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

Author

Listed:
  • Misiorek Adam

    (Institute of Power Systems Automation)

  • Trueck Stefan

    (Queensland University of Technology)

  • Weron Rafal

    (Wroclaw University of Technology)

Abstract

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.

Suggested Citation

  • Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    • Handle: RePEc:bpj:sndecm:v:10:y:2006:i:3:n:2
    DOI: 10.2202/1558-3708.1362
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