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A distributional modelling approach with application to electricity price forecasting — arXiv:2610.01465

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.01465
License (as archived): CC0-1.0
Archived: 2026-10-07T12:29:43.616Z
SHA-256 of the archived text: cacfc8068a02e702196d67aa87d82b681b2e514143c92b14fc365cf8dd0041be
Stable URL of this page: https://metraipolis.com/documento/dc_cece613de734

How to cite

A distributional modelling approach with application to electricity price forecasting — arXiv:2610.01465 — arXiv (en), https://arxiv.org/abs/2610.01465 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cece613de734 (Metraipolis archive dc_cece613de734, sha256 cacfc8068a02e702196d67aa87d82b681b2e514143c92b14fc365cf8dd0041be)

BibTeX

@misc{dccece613de734,
  title = {A distributional modelling approach with application to electricity price forecasting — arXiv:2610.01465},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.01465},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cece613de734; sha256 cacfc8068a02e702196d67aa87d82b681b2e514143c92b14fc365cf8dd0041be},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.01465. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The increasing volatility of electricity prices driven by renewable energy integration, market shocks, and regulatory changes has reinforced the need for forecasting methods that go beyond point predictions and accurately describe the full conditional price distribution. This paper applies the Generalised Additive Models for Location, Scale and Shape (GAMLSS) framework to forecast Spanish day-ahead electricity prices using hourly data from 2020 to 2024. Alternative specifications based on Normal, Johnson's SU (JSU), and Sinh-Arcsinh (SHASH) distributions are considered, allowing the location, scale, and shape parameters to vary with market fundamentals, including electricity demand, renewable generation, seasonal effects, and regulatory and geopolitical risk factors. Forecasts are generated using a rolling-window approach and evaluated through the mean absolute error (MAE), pinball loss, and Diebold-Mariano tests. The results show that flexible distributional specifications improve forecasting performance relative to a naive benchmark and the standard normal specification. While SHASH and JSU specifications provide the lowest point forecasting errors, the hourly analysis reveals substantial intraday variation in relative performance across specifications. JSU specification with all four parameters driven by covariates achieves the best probabilistic forecasting performance, particularly in the tails of the distribution. Diebold-Mariano tests confirm the statistical significance of these improvements. These findings highlight the importance of modelling time-varying shape distributional parameters and demonstrate the value of GAMLSS models for forecasting and risk management in increasingly volatile electricity markets.

Tema degli esploratori: economia dei servizi IA (retro-catalogo arXiv: annunci del 2026-10-02 in econ.EM).
Categorie arXiv: Econometrics (econ.EM).
Pagina del lavoro: https://arxiv.org/abs/2610.01465 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:02:33.889Z dalla pagina https://arxiv.org/catchup/econ.EM/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).