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

Janczura, Joanna, Trück, Stefan, Weron, Rafal and Wolff, Rodney C. (2013) Identifying spikes and seasonal components in electricity spot price data: a guide to robust modeling. Energy Economics, 38 96-110. doi:10.1016/j.eneco.2013.03.013


Author Janczura, Joanna
Trück, Stefan
Weron, Rafal
Wolff, Rodney C.
Title Identifying spikes and seasonal components in electricity spot price data: a guide to robust modeling
Journal name Energy Economics   Check publisher's open access policy
ISSN 0140-9883
1873-6181
Publication date 2013-07-01
Sub-type Article (original research)
DOI 10.1016/j.eneco.2013.03.013
Open Access Status Not yet assessed
Volume 38
Start page 96
End page 110
Total pages 15
Place of publication Netherlands
Publisher Elsevier North-Holland
Language eng
Subject 2002 Economics and Econometrics
2100 Energy
Abstract 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.
Formatted abstract
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.
Keyword Electricity spot price
Outlier treatment
Price spike
Robust modeling
Seasonality
Regime-switching models
Market
Power
Subject
Options
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: W.H. Bryan Mining Geology Research Centre
Official 2014 Collection
 
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