The forecasting accuracy of electricity price formation models

Bunn, D W, Nan, F, Bordignon, S and Lisi, F (2014) The forecasting accuracy of electricity price formation models. International Journal of Energy and Statistics, 2 (1). pp. 1-26. ISSN 2335-6804


In this paper we present an extensive comparison of four different classes of models for daily forecasting of spot electricity prices, including ARMAX, constant and time-varying parameter regression models as well as non-linear Markov regime-switching regressions. They are selected for particular reasons related to the emerging body of research on the price formation processes observed in electricity markets. The analyses are conducted for representative trading periods of the day in the UK Power Exchange prompt market, with the price series adjusted for their deterministic components and spikes. They show that relative out-of-sample forecasting performances are distinctly different for each trading period, season and across the actual performance metrics. No model consistently outperforms the others, but the ARMAX approach performs well in most cases and the Diebold and Mariano test indicates that, when it is not the best, the ARMAX model is not statistically different from the best. Nevertheless, we suggest that subtle differences in performance between different methods under different conditions are consistent with the apparent variations in the price formation processes by time of day and by season. We conclude with some observations on the disparities between the model specifications appropriate for understanding in-sample price formation and those for accurate out-of-sample predictions.

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Item Type: Article
Subject Areas: Management Science and Operations
Date Deposited: 02 Mar 2016 18:51
Last Modified: 19 Apr 2021 15:45

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