  Modeling price risks is crucial for economic decision making in energy
markets. Besides the risk of a single price, the dependence structure of
multiple prices is often relevant. We therefore propose a generic and
easy-to-implement method for creating multivariate probabilistic forecasts
based on univariate point forecasts of day-ahead electricity prices. While each
univariate point forecast refers to one of the day's 24 hours, the multivariate
forecast distribution models dependencies across hours. The proposed method is
based on simple copula techniques and an optional time series component. We
illustrate the method for five benchmark data sets recently provided by Lago et
al. (2020). Furthermore, we demonstrate an example for constructing realistic
prediction intervals for the weighted sum of consecutive electricity prices,
as, e.g., needed for pricing individual load profiles.
