Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling

• Published in: Energy economics Vol. 38; pp. 96 - 110
• Main Authors: Janczura, Joanna, Trück, Stefan, Weron, Rafał, Wolff, Rodney C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2013; Elsevier; Elsevier Science Ltd
• Subjects: Applied sciences; Contracts; Derivatives; Economic data; Economics; Electric energy; Electric power; Electricity; Electricity spot price; Energy; Energy economics; Energy policy; Energy prices; Estimating techniques; Exact sciences and technology; General, economic and professional studies; Methodology. Modelling; Outlier treatment; Price spike; Prices; Rating; Robust modeling; seasonal variation; Seasonality; Securities prices; Spot prices; Stochastic models; stochastic processes; Studies; value added; C52; Price spike; Q47; L94; Outlier treatment; Seasonality; Electricity spot price; C40; C51; Robust modeling
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2013.03.013

An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatments of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification. •First comprehensive study on the impact of spikes on seasonal pattern estimation•The effects of different treatments of spikes on model estimation are examined.•Cleaning spot prices for outliers yields superior estimates of the seasonal pattern.•Removing outliers provides better parameter estimates for the stochastic process.•Rankings of filtering techniques suggested in the literature are provided.