|Institution:||Università degli studi di Bergamo|
|Keywords:||European electricity markets ; electricity prices ; forecasting ; electricity market integration ; multiple time series models; SECS-P/06 - Economia Applicata|
|Full text PDF:||http://hdl.handle.net/10446/31961|
The energy market reform is a complex restructuring process that first has liberalized Member State electricity markets and gradually fosters them toward integration into the Single European Market. Even if national markets are still characterized by several differences in the production structures, regulation shapes a common market design at European level and voluntary measures have been adopted to promote market integration. In this framework, Power Exchanges have taken a key role as shown by the growing volumes traded on their different segments and electricity price forecasting has become an interesting research field. Up to now, most of the contributions on short-term forecasting of day-ahead electricity prices do not include the possibility of dynamic interactions between several interconnected markets, despite the recent empirical literature highlights cointegration in the CWE area. After a primer on the economics of electricity markets and the analysis of the regulatory and market framework, the present work proposes a multiple time series approach for electricity price forecasting, joining the two strands of empirical literature on market integration and day-ahead price forecasting. Accounting for the presence of market integration enlarges the model information set, so it may potentially improve the forecasting performance. This thesis considers hourly day-ahead electricity prices for eight European countries (Austria, Belgium, France, Germany, Italy, Netherlands, Slovenia and Switzerland) for the period May 2010–July 2013. At present, an in-depth comparison between multiple and simple time series forecasting accuracy does not allow stating that estimating multiple time series models, and especially including potential cointegration relationships between day-ahead electricity markets, greatly improve their forecasting performances compared to simple time series models. The adoption of multiple time series may lead to better results only in some hours and in other hours simple time series models outperform multiple time series ones (especially ramp- up hours in the morning).