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

Adam Misiorek, Stefan Trueck () and Rafał Weron ()

Studies in Nonlinear Dynamics & Econometrics, 2006, vol. 10, issue 3, pages 2

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.

Keywords: power market; spot price forecasting; autoregressive model; heteroscedasticity; regime-switching model; threshold autoregression (search for similar items in EconPapers)
JEL-codes: C22 C53 Q40 (search for similar items in EconPapers)
Date: 2006
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