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Portfolio management of a small RES utility with a structural vector autoregressive model of electricity markets in Germany

Author

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  • Katrzyna Maciejowska

Abstract

Electricity producers and traders are exposed to various risks, among which price and volume risk play very important roles. This research considers portfolio-building strategies that enable the proportion of electricity traded in different electricity markets (day-ahead and intraday) to be chosen dynamically. Two types of approaches are considered: a simple strategy, which assumes that these proportions are fixed, and a data-driven strategy, in which the ratios fluctuate. To explore the market information, a structural vector autoregressive model is applied, which allows one to estimate the relationship between the variables of interest and simulate their future distribution. The approach is evaluated using data from the electricity market in Germany. The outcomes indicate that data-driven strategies increase revenue and reduce trading risk. These financial gains may encourage energy traders to apply advanced statistical methods in their portfolio-building process.

Suggested Citation

  • Katrzyna Maciejowska, 2022. "Portfolio management of a small RES utility with a structural vector autoregressive model of electricity markets in Germany," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(4), pages 75-90.
    • Handle: RePEc:wut:journl:v:32:y:2022:i:4:p:75-90:id:5
    DOI: 10.37190/ord220405
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    Citations

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    Cited by:

    1. Katarzyna Maciejowska & Weronika Nitka, 2024. "Multiple split approach -- multidimensional probabilistic forecasting of electricity markets," Papers 2407.07795, arXiv.org.
    2. Weronika Nitka & Rafał Weron, 2023. "Combining predictive distributions of electricity prices. Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(3), pages 105-118.
    3. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    4. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    5. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Papers 2404.03968, arXiv.org.

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