In the following paper we present a simple intensity estimation method of transaction arrivals on the intraday electricity market . Assuming the interarrival times distribution , we utilize a maximum likelihood estimation. The method's performance is briefly tested using German Intraday Continuous data. Despite the simplicity of the method, the results are encouraging. The supplementary materials containing the R-codes and the data are attached to this paper .
In the following paper , we examine the problem of the intensity estimation of transaction arrivals in the intraday electricity markets . Assuming the inter-arrival times distribution and considering multiple intensity function types , we utilize a maximum likelihood estimation. A rolling window forecasting study of future transaction arrival times is conducted in order to gain significant insights to the performance of the considered models. We analyse both the in-sample characteristics and the forecasting efficiency. In the forecasting part, artificial trajectories are simulated and then evaluated by calculating the \mathcal{L .
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we present a simple intensity estimation method
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interarrival times distribution
inter-arrival times distribution and considering multiple intensity function types
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The method's performance is briefly tested using German Intraday Continuous data. Despite the simplicity of the method, the results are encouraging. The supplementary materials containing the R-codes and the data are attached to this paper
A rolling window forecasting study of future transaction arrival times is conducted in order to gain significant insights to the performance of the considered models. We analyse both the in-sample characteristics and the forecasting efficiency. In the forecasting part, artificial trajectories are simulated and then evaluated by calculating the \mathcal{L