Team

Alexander Blasberg

Wissenschaftlicher Mitarbeiter

Alexander Blasberg, M.Sc.

Raum:
R09 R00 H43
Telefon:
+49 201 18-34414
E-Mail:
Sprechstunde:
By arrangement

Lebenslauf:

Professional experience

  • 12/2019–present: Research Assistant, University of Duisburg-Essen, Essen, Chair for Energy Trading and Finance, Prof. Dr. R. Kiesel
  • 10/2017–12/2019: Research AideUniversity of Duisburg-Essen, Essen, Chair for Energy Trading and Finance, Prof. Dr. R. Kiesel
  • 10/2017–12/2019: Research AideUniversity of Duisburg-Essen, Essen, Chair of Econometrics, Prof. Dr. C. Hanck
  • 04/2017–09/2017: Student AssistantUniversity of Duisburg-Essen, Essen, Chair for Energy Trading and Finance, Prof. Dr. R. Kiesel
  • 04/2016–09/2017: Student AssistantUniversity of Duisburg-Essen, Essen, Chair of Econometrics, Prof. Dr. C. Hanck
  • 10/2014–01/2015: Student AssistantUniversity of Duisburg-Essen, Essen, Chair of Econometrics, Prof. Dr. C. Hanck

Education

  • 2019–present: Ph.D. Student in Mathematical Finance, University of Duisburg-Essen, Essen, Chair for Energy Trading and Finance, Prof. Dr. R. Kiesel
  • 2017–2019: M.Sc. Energy & Finance, University of Duisburg-Essen, Essen, with distinction
  • 2013–2017: B.Sc. Business Administration, University of Duisburg-Essen, Essen

Publikationen:

Filter:
  • A. Blasberg; N. Graf von Luckner; R. Kiesel: Modeling the Serial Structure of the Hawkes Process Parameters for Market Order Arrivals on the German Intraday Power Market. In: 16th International Conference on the European Energy Market (EEM) (2019), S. 1-6. doi:10.1109/EEM.2019.8916326 Volltext BIB Download Details

    Existing research indicates that on the intraday market for power deliveries in Germany market orders tend to arrive in clusters. To capture such clustering, point processes with an intensity depending on past events, so-called Hawkes processes, appear to be promising. We consider the question whether there is a temporal structure prevalent in the parameters of Hawkes processes estimated for adjacent delivery hours. First we model a diurnal seasonality pattern found in the data and provide an economic intepretation for it. For the remaining decomposed series, we then propose simple (vector) autoregressive models to describe the serial structure. To evaluate our model we conduct a forecasting study. Testing against a benchmark model and a model without any serial structure, we find evidence for our proposed model. Our study reveals that capturing the serial structure in the parameters proves to be useful in understanding the underlying market microstructure.

Lehrveranstaltungen:

WS 2019/20: Energy Trading