Aitor Ciarreta, Peru Muniain, Ainhoa Zarraga
arXiv 1 Oct 2026 · Econometrics
arXiv:2610.01465 · PDF · Extracted main text
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.
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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 | |
|---|---|---|---|---|---|
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| 2 | Nowotarski, J. and Weron, R (2018) Recent Advances in Electricity Price Forecasting: A Review of Probabilistic Forecasting | 0.737 | 3 | 2 | 100% |
| 3 | Weron, R (2014) Electricity Price Forecasting: A Review of the State-of-the-Art with a Look into the Future | 0.737 | 3 | 2 | 100% |
| 4 | Rigby, R.A. and Stasinopoulos, D.M (2005) Generalized Additive Models for Location, Scale and Shape | 0.644 | 2 | 2 | 100% |
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| 7 | Cifter, A (2013) Forecasting electricity price volatility with the Markov-switching GARCH model: Evidence from the Nordic electric power market | 0.405 | 1 | 1 | 100% |
| 8 | J. Contreras and R. Espínola and F. J. Nogales and A. J. Conejo (2003) ARIMA Models to Predict Next-Day Electricity Prices | 0.405 | 1 | 1 | 100% |
| 9 | European Commission (2023) Proposal for a Regulation of the European Parliament and of the Council amending Regulations (EU) 2019/943 and (EU) 2019/942 as… | 0.405 | 1 | 1 | 100% |
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