arXiv 23 Nov 2022 · Finance — Statistical Finance · publishedThe Energy Journal (2023) · 12 citations (OpenAlex)
arXiv:2211.13002 · PDF · DOI · OpenAlex · Extracted main text
During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Micha Narajewski and Florian Ziel (2020) Ensemble forecasting for intraday electricity prices: Simulating trajectories self | 1.000 | 19 | 7 | 100% |
| 2 | Marcel Kremer, Rüdiger Kiesel, and Florentina Paraschiv (2020) Intraday Electricity Pricing of Night Contracts | 1.000 | 9 | 3 | 100% |
| 3 | Marcel Kremer, Rüdiger Kiesel, and Florentina Paraschiv (2021) An econometric model for intraday electricity trading | 1.000 | 8 | 3 | 100% |
| 4 | Micha Narajewski and Florian Ziel (2019) Econometric Modelling and Forecasting of Intraday Electricity Prices self | 1.000 | 6 | 3 | 100% |
| 5 | Sergei Kulakov and Florian Ziel (2019) Determining Fundamental Supply and Demand Curves in a Wholesale Electricity Market self | 0.874 | 9 | 2 | 100% |
| 6 | Jakub Nowotarski and Rafa Weron (2017) Recent Advances in Electricity Price Forecasting: A Review of Probabilistic Forecasting | 0.874 | 7 | 2 | 100% |
| 7 | Clara Balardy (2022) An empirical analysis of the bid-ask spread in the continuous intraday trading of the german power market | 0.874 | 6 | 2 | 100% |
| 8 | Sergei Kulakov and Florian Ziel (2020) The Impact of Renewable Energy Forecasts on Intraday Electricity Prices self | 0.874 | 6 | 2 | 100% |
| 9 | Tim Janke and Florian Steinke (2019) Forecasting the price distribution of continuous intraday electricity trading | 0.811 | 4 | 2 | 100% |
| 10 | Thomas Kuppelwieser and David Wozabal (2021) Intraday Power Trading: Towards an Arms Race in Weather Forecasting? | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 72 scored citations.