arXiv 23 Jun 2023 · Finance — Statistical Finance · publishedApplied Stochastic Models in Business and Industry (2024) · 10 citations (OpenAlex)
arXiv:2306.13419 · PDF · DOI · OpenAlex · Extracted main text
Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross-product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high-dimensional intraday price return vector. We model the marginal distribution as a zero-inflated Johnson's $S_U$ distribution with location, scale and shape parameters that depend on market and fundamental data. The dependence structure is modelled using latent beta regression to account for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days. We allow the dependence parameter to be time-varying. We validate our approach in a simulation study for the German intraday electricity market and find that modelling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling (SIDC) on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.
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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 | S. Hirsch and F. Ziel (2023) Simulation-based forecasting for intraday power markets: Modelling fundamental drivers for location, shape and scale of the pric… | 1.000 | 24 | 6 | 100% |
| 2 | M. Narajewski and F. Ziel (2020) Ensemble forecasting for intraday electricity prices: Simulating trajectories | 1.000 | 17 | 6 | 100% |
| 3 | M. Narajewski and F. Ziel (2020) Econometric modelling and forecasting of intraday electricity prices | 1.000 | 7 | 4 | 100% |
| 4 | N. Löhndorf and D. Wozabal (2023) The value of coordination in multimarket bidding of grid energy storage | 0.874 | 5 | 2 | 100% |
| 5 | M. Kremer, R. Kiesel, and F. Paraschiv (2021) An econometric model for intraday electricity trading | 0.843 | 3 | 3 | 100% |
| 6 | N. Nolzen, A. Ganter, N. Baumgärtner, L. Leenders, and A. Bardow (2022) Where to market flexibility? optimal participation of industrial energy systems in balancing-power, day-ahead, and continuous in… | 0.843 | 3 | 3 | 100% |
| 7 | T. Janke and F. Steinke (2019) Forecasting the price distribution of continuous intraday electricity trading | 0.737 | 3 | 2 | 100% |
| 8 | C. Kath (2019) Modeling intraday markets under the new advances of the cross-border intraday project (xbid): Evidence from the german intraday… | 0.737 | 3 | 2 | 100% |
| 9 | B. Uniejewski, G. Marcjasz, and R. Weron (2019) Understanding intraday electricity markets: Variable selection and very short-term price forecasting using lasso | 0.737 | 3 | 2 | 100% |
| 10 | J. Berrisch, S. Pappert, F. Ziel, and A. Arsova (2023) Modeling volatility and dependence of european carbon and energy prices | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.