Forecasting Daily Electricity Prices with Monthly Macroeconomic Variables

61 Pages Posted: 21 Mar 2019

See all articles by Claudia Foroni

Claudia Foroni

European Central Bank (ECB); Centre for Economic Policy Research (CEPR)

Francesco Ravazzolo

Free University of Bozen-Bolzano; BI Norwegian Business School - Department of Data Science and Analytics

Luca Rossini

University of Milan; Ca Foscari University of Venice - Dipartimento di Economia

Date Written: March 20, 2019

Abstract

We analyse the importance of macroeconomic information, such as industrial production index and oil price, for forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse unrestricted MIDAS models (RU-MIDAS). We study the forecasting accuracy for different horizons (from 1 day ahead to 28 days ahead) and by considering different specifications of the models. We find gains around 20% at short horizons and around 10% at long horizons. Therefore, it turns out that the macroeconomic low frequency variables are more important for short horizons than for longer horizons. The benchmark is almost never included in the model confidence set.

Keywords: Density Forecasting, Electricity Prices, Forecasting, Mixed-Frequency VAR models, MIDAS models

JEL Classification: C11, C53, Q43, Q47

Suggested Citation

Foroni, Claudia and Ravazzolo, Francesco and Rossini, Luca, Forecasting Daily Electricity Prices with Monthly Macroeconomic Variables (March 20, 2019). ECB Working Paper No. 2250 (2019); ISBN 978-92-899-3512-8 , Available at SSRN: https://ssrn.com/abstract=3357361 or http://dx.doi.org/10.2139/ssrn.3357361

Claudia Foroni (Contact Author)

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

Francesco Ravazzolo

Free University of Bozen-Bolzano ( email )

Sernesiplatz 1
Bozen-Bolzano, BZ 39100
Italy

BI Norwegian Business School - Department of Data Science and Analytics ( email )

Luca Rossini

University of Milan ( email )

Via Festa del Perdono, 7
Milan, 20122
Italy

Ca Foscari University of Venice - Dipartimento di Economia ( email )

Cannaregio 873
Venice, 30121
Italy

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