Predictive densities for day-ahead electricity prices using time-adaptive quantile regression

Tryggvi Jónsson, Pierre Pinson, Henrik Madsen, Henrik Aalborg Nielsen

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A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for such prices, then enhanced with a temporal dependence structure. A semi-parametric methodology for generating such densities is presented: it includes: (i) a time-adaptive quantile regression model for the 5%–95% quantiles; and (ii) a description of the distribution tails with exponential distributions. The forecasting skill of the proposed model is compared to that of four benchmark approaches and the well-known the generalist autoregressive conditional heteroskedasticity (GARCH) model over a three-year evaluation period. While all benchmarks are outperformed in terms of forecasting skill overall, the superiority of the semi-parametric model over the GARCH model lies in the former’s ability to generate reliable quantile estimates.
Original languageEnglish
Issue number9
Pages (from-to)5523-5547
Publication statusPublished - 2014


  • Stochastic processes
  • Electricity prices
  • Density forecasting
  • Quantile regression
  • Non-stationarity

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