  Intraday electricity markets play an increasingly important role in balancing
the intermittent generation of renewable energy resources, which creates a need
for accurate probabilistic price forecasts. However, research to date has
focused on univariate approaches, while in many European intraday electricity
markets all delivery periods are traded in parallel. Thus, the dependency
structure between different traded products and the corresponding cross-product
effects cannot be ignored. We aim to fill this gap in the literature by using
copulas to model the high-dimensional intraday price return vector. We model
the marginal distribution as a zero-inflated Johnson's $S_U$ distribution with
location, scale and shape parameters that depend on market and fundamental
data. The dependence structure is modelled using latent beta regression to
account for the particular market structure of the intraday electricity market,
such as overlapping but independent trading sessions for different delivery
days. We allow the dependence parameter to be time-varying. We validate our
approach in a simulation study for the German intraday electricity market and
find that modelling the dependence structure improves the forecasting
performance. Additionally, we shed light on the impact of the single intraday
coupling (SIDC) on the trading activity and price distribution and interpret
our results in light of the market efficiency hypothesis. The approach is
directly applicable to other European electricity markets.
