 Distributional forecasting of electricity DART spreads with a covariate-dependent mixture modelAnthony Forgetta, Frédéric Godin and Maciej Augustyniak Energy Economics, 2025, vol. 144, issue C Abstract: We develop a covariate-dependent mixture model to describe the behavior of electricity DART spreads, which are differentials between day-ahead and real-time prices of electricity. The model includes three regimes: a regular DART regime, a positive spike regime, and a negative spike regime. The model exhibits sufficient flexibility to allow covariates impacting both the frequency and severity of DART spread spikes, and to reproduce salient stylized facts of DART spread dynamics. The covariates considered include forecasts for load, weather, and natural gas prices. The application of our model on data from the Long Island zone of the NYISO (New York Independent System Operator) exhibits a satisfactory fit to the data. Numerical experiments reveal that including covariates in the severity component of the model is crucial, while mild additional performance is obtained with their inclusion in the frequency component. Furthermore, neural network-based quantile regression benchmarks are unable to improve performance over our mixture model. Keywords: Electricity markets; Mixture models; Risk management; Distributional forecasts (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325001562 DOI: 10.1016/j.eneco.2025.108332 Access Statistics for this article Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant More articles in Energy Economics from Elsevier |
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