Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets
Introduction
Electricity is a standardized cross-border traded commodity. Especially in Europe, where an ongoing market integration between countries proceeds rapidly, national markets cannot be considered as one isolated trading place.
Several authors have studied the relation of European electricity markets empirically within the past years. For instance, using price data of forward markets (Bunn and Gianfreda, 2010) showed by analyzing cross-market interactions of some of the major European electricity markets that they are integrated. Moreover, they provide evidence for an increase in this integration over time. The German electricity market turned out to be the most integrated market. According to their study the integration is not necessarily reliant on sharing a geographical border: The Spanish and German market, for instance, seemed to transmit shocks as well. The important role of the German electricity market for other European markets was also pointed out by Bollino et al. (2013). Using cointegration techniques they find that the German electricity price embodies a price signal for the other investigated European markets, e.g. France and Italy.
Even though the hypothesis of market integration for some of the major markets seems to be satisfied (see also (Bosco et al., 2010, Kalantzis and Milonas, 2010) or (Houllier and de Menezes, 2012)), it is debatable if this holds true for every European market. Zachmann (2008) as well as Huisman and Kiliç (2013) argued that especially some of the Scandinavian electricity prices are behaving differently. This issue was also analyzed in detail by Ferkingstad et al. (2011). They were able to show that at least for the weekly time series of Nordic and German electricity prices a connection through gas prices is present.
In our paper we exploit those findings by combining them with the different specifications of the European exchanges. As the price results for the day-ahead auction of each of these markets are revealed at different points in time, even though the same trading period is considered, we use the relationship of those markets to improve common modeling approaches. As basic electricity exchange we focus on the Energy Exchange Austria (EXAA) for two reasons. First, the EXAA discloses day-ahead prices prior to most of the other European exchanges which are connected with Germany and Austria. Second, the EXAA contains a special case of price relations, where not only the same time period is traded prior to other markets but also the same market region. This holds true for the European Power Exchange (EPEX) and the EXAA, as both cover Germany and Austria. The EXAA reveals their prices approximately at 10:20 pm, whereas offers to the EPEX can be submitted until 12:00 pm. These EXAA prices are the prices for all days ahead up to the next working day, which can be e.g. three days at once for weekends. If there is a systematic relation between both markets present, traders could use the price information of the EXAA to adjust their bidding structure. This approach is applied to many European exchanges. As the EXAA covers Germany and Austria, we focus only on these European markets, which are directly connected with Germany and Austria.
The existent literature concerning the usage of the early price disclosure of the EXAA is very scarce. It was only discussed in the framework of forward risk premiums, for instance in (Ronn and Wimschulte, 2009) or (Erni, 2012). In these studies the EXAA price is usually regarded as an early price signal for electricity of the EPEX or European Energy Exchange (EEX) respectively. Viehmann (2011) for instance considers the EXAA prices as a snapshot for the German and Austrian electricity price traded via over-the-counter (OTC) business. However, a direct application of the EXAA price into modeling the electricity price of other European markets has, to our knowledge, not been done so far.
Our paper closes this gap by considering the time series of EXAA electricity prices as an external regressor. Because, in an econometric modeling framework, autoregressive models turn out to be of superior model performance, we will estimate the most common basic approaches and compare them with their counterparts when the EXAA electricity price is used as influencing variable.
Therefore we organized our paper as follows. In Section 2 we will describe the different data sets and exchange specifications. Moreover, we will provide information about the necessary data arrangement for using the EXAA price as a regressor. The subsequent section will then introduce the econometric models which are applied to our data set. In Section 4 we will employ a comprehensive forecasting study for every examined market place. Section 5 discusses our findings and analyses the temporal structure of the relationship. We will focus on short term forecasting of one day ahead. The last section summarizes our results and grants insights for future research.
Throughout the whole paper we will use bold symbols for multivariate expressions and normal symbols for univariate objects.
Section snippets
The considered electricity markets
In order to measure the impacts of the EXAA day-ahead price on other electricity exchanges, it is mandatory to determine a feasible set of these exchanges. In our case we decided to use exchanges, which are directly connected with Germany and Austria, as the EXAA covers both countries. According to the transparency platform of the European Network of Transmission System Operators for Electricity (ENTSOE) there are 10 different countries with a cross border physical flow to either Germany or
Models for electricity prices
In the following we present several models for . These models are easily structured and based on two very common modeling approaches. They are the persistent or naïve model and the autoregressive process of order p–AR(p). Especially the AR(p) modeling approach is considered as fundamental for an econometric analysis of electricity prices. The process itself or slightly modified versions of it are very often used in the literature, for instance in (Weron and Misiorek (2008), Ferkingstad et
Setup of the forecasting study
For evaluating the forecasting performance and the desired impact of the EXAA price we carry out a forecasting study. We face the situation that we sometimes have to forecast 23 or 25 prices instead of 24, which complicates the notation and forecasting. Nevertheless, the occurrence of such specific days is considered within the analysis. As mentioned previously the available data covers 7 × 365 = 2555 days which is about 7 years.
In the forecasting study we use a rolling window of hourly data (Y1 + R(r)
Results and discussion
All results are based on out-of-sample data. The estimated MAE and RMSE are given in Table 3. Every bold print number corresponds to the best model in terms of MAE or RMSE. An underlined value represents a model, which produced a MAE or RMSE which was at least in the confidence interval of the best model. The number in brackets shows the standard deviation, which was estimated via bootstrapping with a sample size of B = 1000. First of all we can observe that the standard deviations of the MAE
Conclusion
We investigated several models to show the impact of the EXAA day-ahead price on electricity day-ahead spot prices of regions, which are directly connected to Germany and Austria. To conduct our study we introduced a unique investigation setting, where traders can utilize different price settlement time points of exchanges to get a snapshot of other markets. By analyzing different error metrics and test setups we were also able to provide insights in the relatedness of those markets. It turned
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