Multivariate probabilistic forecasting of electricity prices with trading applications

Ilyas Agakishiev, Wolfgang Karl Härdle, Milos Kopa, Karel Kozmik and Alla Petukhina

Energy Economics, 2025, vol. 141, issue C

Abstract: This study extends recently introduced neural networks approach, based on a regularized distributional multilayer perceptron (DMLP) technique for a multivariate case electricity price forecasting. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. Different from previous studies we incorporate dependence between multiple exchanges (EPEX and Nord Pool). The empirical data application analyzes two auctions in the day-ahead electricity market for the United Kingdom market. Along with statistical evaluation of probabilistic forecasts we develop a flexible bidding strategy based on risk-adjusted investor utility function. The trading application leverages the differences of the two exchanges by having long/short positions in both. Our findings demonstrate while DMLP shows similar performance compared to the benchmarks, the algorithm is considerably less computationally costly. LASSO Quantile Regression is better in terms if statistical evaluation of distributional fit, while DMLP outperforms in terms of Sharpe ratio (by 18%) in the trading application.

Keywords: Electricity market; Distributional modeling; Simulation; Trading strategies; Probabilistic forecasting (search for similar items in EconPapers)
JEL-codes: C22 C44 C45 C46 C53 Q47 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:141:y:2025:i:c:s0140988324007163

DOI: 10.1016/j.eneco.2024.108008

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