  Probabilistic price forecasting has recently gained attention in power
trading because decisions based on such predictions can yield significantly
higher profits than those made with point forecasts alone. At the same time,
methods are being developed to combine predictive distributions, since no model
is perfect and averaging generally improves forecasting performance. In this
article we address the question of whether using CRPS learning, a novel
weighting technique minimizing the continuous ranked probability score (CRPS),
leads to optimal decisions in day-ahead bidding. To this end, we conduct an
empirical study using hourly day-ahead electricity prices from the German EPEX
market. We find that increasing the diversity of an ensemble can have a
positive impact on accuracy. At the same time, the higher computational cost of
using CRPS learning compared to an equal-weighted aggregation of distributions
is not offset by higher profits, despite significantly more accurate
predictions.

Statistical Finance;Econometrics;Computation;Machine Learning