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

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-ofsample 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. * The authors are grateful to Dick van Dijk and two anonymous referees for insightful comments and suggestions. This work was supported in part by KBN grant 4-T10B-030-25 (to Misiorek).

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

“…Traditional autoregressive time series models treat spikes through Poisson and Bernoulli jump processes [23], the inclusion of thresholds [24] or the use of different multivariate error distributions [16]. Meanwhile, regime-switching models are the nonlinear extension of traditional time series.…”

Fitting semiparametric Markov regime-switching models to electricity spot prices

“…As already pointed out to overcome this problem, different model specifications have been proposed in the past (for overviews see e.g. Misiorek et al, 2006, Bierbrauer et al, 2007and De Jong, 2006. The possibly most successful one is the MS model of Huisman and De Jong (2003) on which we are going to extend by loosening its distributional assumption for the spike process.…”

“…As shown, for example, by De Jong (2006) and Misiorek et al (2006) these models seem to be better suited to account for the distinct nature of electricity prices. In this context it is not surprising that a lot of research has been focused on the extension and evaluation of different regime-switching models.…”

“…In this context it is not surprising that a lot of research has been focused on the extension and evaluation of different regime-switching models. Rambharat et al (2005) and Misiorek et al (2006) extended the TAR model by an exogenous variable (temperature recorded at the same time as the maximum price of the day and load). For MS models Weron et al (2004) and Bierbrauer et al (2007) compared the model introduced by Huisman and De Jong (2003) using different distributional assumptions for the spike process, while Misiorek et al (2006) compared the regime-switching model with other model classes evaluating their short-term forecasting power.…”

“…Rambharat et al (2005) and Misiorek et al (2006) extended the TAR model by an exogenous variable (temperature recorded at the same time as the maximum price of the day and load). For MS models Weron et al (2004) and Bierbrauer et al (2007) compared the model introduced by Huisman and De Jong (2003) using different distributional assumptions for the spike process, while Misiorek et al (2006) compared the regime-switching model with other model classes evaluating their short-term forecasting power. Extensions were for example given by Kosater and Mosler (2006) who introduced a third regime to account for the occurrence of negative spikes and by Mount et al (2006) who allowed the transition probabilities to depend on the load and/or the implicit reserve margin.…”