Arbeitspapier

Deregulated day-ahead electricity markets in Southeast Europe: Price forecasting and comparative structural analysis

Many Southeast European countries are currently undergoing a process of liberalization of electric power markets. The paper analyses day-ahead price dynamics on some of these new markets and in Germany as a benchmark of a completely decentralized Western European market. To that end, several price forecasting methods including autoregressive approaches, multiple linear regression, and neural networks are considered. These methods are tested on hourly day-ahead price data during four two-week periods corresponding to different seasons and varying levels of volatility in all selected markets. The most influential fundamental factors are determined and performance of forecasting techniques is analysed with respect to the age of the market, its degree of liberalization, and the level of volatility. A comparison of Southeast European electricity markets of different age with the older German market is made and clusters of similar Southeast European markets are identified.

Sprache
Englisch

Erschienen in
Series: IRTG 1792 Discussion Paper ; No. 2018-009

Klassifikation
Wirtschaft
Mathematical and Quantitative Methods: General
Thema
ARIMA models
energy forecasting
time series models
neural networks

Ereignis
Geistige Schöpfung
(wer)
Hryshchuk, Antanina
Lessmann, Stefan
Ereignis
Veröffentlichung
(wer)
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
(wo)
Berlin
(wann)
2018

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Hryshchuk, Antanina
  • Lessmann, Stefan
  • Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Entstanden

  • 2018

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