Point and interval forecasting of spot electricity prices: Linear vs. non-linear time series models

Adam Misiorek*, Stefan Trueck, Rafal Weron

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

137 Citations (Scopus)

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.

Original languageEnglish
Article number2
Pages (from-to)1-34
Number of pages34
JournalStudies in Nonlinear Dynamics and Econometrics
Volume10
Issue number3
Publication statusPublished - 2006
Externally publishedYes

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