Forecasting daily electricity prices with monthly macroeconomic variables

Claudia Foroni, Francesco Ravazzolo, L. Rossini

Research output: Working paperAcademic

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.
Original languageEnglish
PublisherWorking paper
Publication statusPublished - 20 Mar 2019

Publication series

NameECB Working Paper Series

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Macroeconomic variables
Electricity price
Macroeconomics
Forecasting accuracy
Oil prices
Benchmark
Germany
Confidence set
Bayesian approach
Industrial production
Italy

Cite this

Foroni, C., Ravazzolo, F., & Rossini, L. (2019). Forecasting daily electricity prices with monthly macroeconomic variables. (ECB Working Paper Series). Working paper.
Foroni, Claudia ; Ravazzolo, Francesco ; Rossini, L. / Forecasting daily electricity prices with monthly macroeconomic variables. Working paper, 2019. (ECB Working Paper Series).
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Foroni, C, Ravazzolo, F & Rossini, L 2019 'Forecasting daily electricity prices with monthly macroeconomic variables' ECB Working Paper Series, Working paper.

Forecasting daily electricity prices with monthly macroeconomic variables. / Foroni, Claudia; Ravazzolo, Francesco; Rossini, L.

Working paper, 2019. (ECB Working Paper Series).

Research output: Working paperAcademic

TY - UNPB

T1 - Forecasting daily electricity prices with monthly macroeconomic variables

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AU - Ravazzolo, Francesco

AU - Rossini, L.

PY - 2019/3/20

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N2 - 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.

AB - 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.

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Foroni C, Ravazzolo F, Rossini L. Forecasting daily electricity prices with monthly macroeconomic variables. Working paper. 2019 Mar 20. (ECB Working Paper Series).