scholarly journals Modeling Market Order Arrivals on the German Intraday Electricity Market with the Hawkes Process

2021 ◽  
Vol 14 (4) ◽  
pp. 161
Author(s):  
Nikolaus Graf von Luckner ◽  
Rüdiger Kiesel

We use point processes to analyze market order arrivals on the intraday market for hourly electricity deliveries in Germany in the second quarter of 2015. As we distinguish between buys and sells, we work in a multivariate setting. We model the arrivals with a Hawkes process whose baseline intensity comprises either only an exponentially increasing component or a constant in addition to the exponentially increasing component, and whose excitation decays exponentially. Our goodness-of-fit tests indicate that the models where the intensity of each market order type is excited at least by events of the same type are the most promising ones. Based on the Akaike information criterion, the model without a constant in the baseline intensity and only self-excitation is selected in almost 50% of the cases on both market sides. The typical jump size of intensities in case of the arrival of a market order of the same type is quite large, yet rather short lived. Diurnal patterns in the parameters of the baseline intensity and the branching ratio of self-excitation are observable. Contemporaneous relationships between different parameters such as the jump size and decay rate of self and cross-excitation are found.

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 447
Author(s):  
Riccardo Rossi ◽  
Andrea Murari ◽  
Pasquale Gaudio ◽  
Michela Gelfusa

The Bayesian information criterion (BIC), the Akaike information criterion (AIC), and some other indicators derived from them are widely used for model selection. In their original form, they contain the likelihood of the data given the models. Unfortunately, in many applications, it is practically impossible to calculate the likelihood, and, therefore, the criteria have been reformulated in terms of descriptive statistics of the residual distribution: the variance and the mean-squared error of the residuals. These alternative versions are strictly valid only in the presence of additive noise of Gaussian distribution, not a completely satisfactory assumption in many applications in science and engineering. Moreover, the variance and the mean-squared error are quite crude statistics of the residual distributions. More sophisticated statistical indicators, capable of better quantifying how close the residual distribution is to the noise, can be profitably used. In particular, specific goodness of fit tests have been included in the expressions of the traditional criteria and have proved to be very effective in improving their discriminating capability. These improved performances have been demonstrated with a systematic series of simulations using synthetic data for various classes of functions and different noise statistics.


1992 ◽  
Vol 19 (4) ◽  
pp. 616-626 ◽  
Author(s):  
K. C. Ander Chow ◽  
W. Edgar Watt

When conducting a conventional single-station flood frequency analysis, an appropriate distribution must be selected. Typically, sample statistics, probability plots, goodness-of-fit tests, etc. are used to facilitate the decision process. For the predominate case of a relatively short record length of flood series, this standard approach leads to undue emphasis on goodness of fit and virtually no consideration of the uncertainty due to additional parameters. The information criterion suggested by Akaike (AIC) is a measure to evaluate the "benefit" of goodness of fit and the "cost" of parameter uncertainty. The criterion is tested for 42 long-term hydrometric stations across Canada and its applicability and limitations are demonstrated in eight samples. The AIC is recommended as an aid in selecting a flood frequency distribution. Key words: flood frequency, goodness of fit, single station, information criterion.


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