  Commodity price time series possess interesting features, such as
heavy-tailedness, skewness, heteroskedasticity, and non-linear dependence
structures. These features pose challenges for modeling and forecasting. In
this work, we explore how spatio-temporal copula-based time series models can
be effectively employed for these purposes. We focus on price series for fossil
fuels and carbon emissions. Further, we illustrate how the t-copula may be used
in conditional heteroskedasticity modeling. The possible emergence of
non-elliptical probabilistic forecasts in this context is examined and
visualized. The problem of finding an appropriate point forecast given a
non-elliptical probabilistic forecast is discussed. We propose a solution where
the forecast is augmented with an artificial neural network (ANN). The ANN
predicts the best (in MSE sense) quantile to use as point forecast. In a
forecasting study, we find that the copula-based models are competitive.
