Michał Narajewski, Florian Ziel
arXiv 4 May 2020 · Finance — Statistical Finance · publishedApplied Energy (2020) · 62 citations (OpenAlex)
arXiv:2005.01365 · PDF · DOI · OpenAlex · Extracted main text
Recent studies concerning the point electricity price forecasting have shown evidence that the hourly German Intraday Continuous Market is weak-form efficient. Therefore, we take a novel, advanced approach to the problem. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window to receive a realistic ensemble to allow for more efficient intraday trading and redispatch. A generalized additive model is fitted to the price differences with the assumption that they follow a zero-inflated distribution, precisely a mixture of the Dirac and the Student's t-distributions. Moreover, the mixing term is estimated using a high-dimensional logistic regression with lasso penalty. We model the expected value and volatility of the series using i.a. autoregressive and no-trade effects or load, wind and solar generation forecasts and accounting for the non-linearities in e.g. time to maturity. Both the in-sample characteristics and forecasting performance are analysed using a rolling window forecasting study. Multiple versions of the model are compared to several benchmark models and evaluated using probabilistic forecasting measures and significance tests. The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets, especially in Europe. The results prove superiority of the mixture model over the benchmarks gaining the most from the modelling of the volatility. They also indicate that the introduction of XBID reduced the market volatility.
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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 | M. Narajewski and F. Ziel (2019) Econometric modelling and forecasting of intraday electricity prices | 1.000 | 10 | 5 | 100% |
| 2 | C. Kath (2019) Modeling intraday markets under the new advances of the cross-border intraday project (XBID): Evidence from the German intraday… | 0.928 | 4 | 3 | 100% |
| 3 | B. Uniejewski, G. Marcjasz, and R. Weron Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO | 0.737 | 3 | 2 | 100% |
| 4 | R. Tibshirani (1996) Regression Shrinkage and Selection via the Lasso | 0.644 | 2 | 2 | 100% |
| 5 | J. Viehmann (2017) State of the German Short-Term Power Market | 0.644 | 2 | 2 | 100% |
| 6 | T. Hastie and R. Tibshirani (1990) Generalized Additive Models, volume 43 | 0.644 | 2 | 2 | 100% |
| 7 | T. Janke and F. Steinke (2019) Forecasting the price distribution of continuous intraday electricity trading | 0.644 | 2 | 2 | 100% |
| 8 | P. Muniain and F. Ziel (2020) Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices | 0.644 | 2 | 2 | 100% |
| 9 | J. Nowotarski and R. Weron (2018) Recent advances in electricity price forecasting: A review of probabilistic forecasting | 0.644 | 2 | 2 | 100% |
| 10 | R. A. Rigby and D. M. Stasinopoulos (2005) Generalized additive models for location, scale and shape | 0.644 | 2 | 2 | 100% |
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