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

Forecasting Natural Gas Prices with Spatio-Temporal Copula-based Time Series Models