In the following paper, we analyse the ID 3 -Price in the German Intraday Continuous electricity market using an econometric time series model. A multivariate approach is conducted for hourly and quarter-hourly products separately. We estimate the model using lasso and elastic net techniques and perform an out-of-sample, very short-term forecasting study. The model's performance is compared with benchmark models and is discussed in detail. Forecasting results provide new insights to the German Intraday Continuous electricity market regarding its efficiency and to the ID 3 -Price behaviour.

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We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple timevarying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and the probabilistic forecasting performance. In a rolling window forecasting study, we simulate many trajectories to evaluate the forecasts and gain significant insights into the model fit. The prediction accuracy is evaluated by a functional version of the MAE (mean absolute error), RMSE (root mean squared error) and CRPS (continuous ranked probability score) for the simulated count processes. This paper fills the gap in the literature regarding the intensity estimation of transaction arrivals and is a major contribution to the topic, yet leaves much of the field for further development. The study presented in this paper is conducted based on the German Intraday Continuous electricity market data, but this method can be easily applied to any other continuous intraday electricity market. For the German market, a specific generalized gamma distribution setup explains the overall behaviour significantly best, especially as the tail behaviour of the process is well covered.

Electricity exchanges offer several trading possibilities for market participants: starting with futures products through the spot market consisting of the auction and continuous part, and ending with the balancing market. This variety of choice creates a new question for traders -when to trade to maximize the gain. This problem is not trivial especially for trading larger volumes as the market participants should also consider their own price impact. The following paper raises this issue considering two markets: the hourly EPEX Day-Ahead Auction and the quarter-hourly EPEX Intraday Auction. We consider a realistic setting which includes a forecasting study and a suitable evaluation. For a meaningful optimization many price scenarios are considered that we obtain using bootstrap with models that are well-known and researched in the electricity price forecasting literature. The own market impact is predicted by mimicking the demand or supply shift in the respectful auction curves. A number of trading strategies is considered, e.g. minimization of the trading costs, risk neutral or risk averse agents. Additionally, we provide theoretical results for risk neutral agents. Especially we show when the optimal trading path coincides with the solution that minimizes transaction costs. The application study is conducted using the German market data, but the presented methods can be easily utilized with other two auction-based markets. They could be also generalized to other market types, what is discussed in the paper as well. The empirical results show that market participants could increase their gains significantly compared to simple benchmark strategies.

We present a novel approach to probabilistic electricity price forecasting (EPF) which utilizes distributional artificial neural networks. The novel network structure for EPF is based on a regularized distributional multilayer perceptron (DMLP) which contains a probability layer. Using the TensorFlow Probability framework, the neural network's output is defined to be a distribution, either normal or potentially skewed and heavy-tailed Johnson's SU (JSU). The method is compared against state-of-the-art benchmarks in a forecasting study. The study comprises forecasting involving day-ahead electricity prices in the German market. The results show evidence of the importance of higher moments when modeling electricity prices.