Go to the content. | Move to the navigation | Go to the site search | Go to the menu | Contacts | Accessibility

| Create Account

Nan, Fany and Bordignon, Silvano and Bunn, Derek W. and Lisi, Francesco (2010) The Forecasting Accuracy of Electricity Price Formation Models. [Working Paper] WORKING PAPER SERIES, 4/2010 . , PADOVA (Inedito)

Full text disponibile come:

PDF Document

Abstract (english)

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.

Statistiche Download - Aggiungi a RefWorks
EPrint type:Working Paper
Anno di Pubblicazione:27 April 2010
Key Words:Forecasting, Electricity, Prices, ARMAX, Regime-Switching, Time-Varying Parameters, Accuracy.
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:7164
Depositato il:15 Sep 2014 14:19
Simple Metadata
Full Metadata
EndNote Format

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record