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.

翻译：日内电力市场在平衡可再生能源间歇性发电中扮演着日益重要的角色，这促使对精确的概率价格预测产生需求。然而，现有研究主要集中于单变量方法，而在许多欧洲日内电力市场中，所有交割时段均并行交易。因此，不同交易产品之间的依赖结构及相应的跨产品效应不容忽视。我们旨在通过使用Copula模型对高维日内价格收益向量进行建模以填补这一文献空白。我们采用零膨胀约翰逊$S_U$分布对边际分布建模，其位置、尺度及形状参数取决于市场与基本面数据。依赖结构则通过潜在贝塔回归建模，以刻画日内电力市场的特定市场结构（例如不同交割日的重叠但独立的交易时段）。我们允许依赖参数随时间变化。通过针对德国日内电力市场的仿真研究验证所提方法，发现建模依赖结构可提升预测性能。此外，我们揭示了单一日内耦合（SIDC）对交易活动与价格分布的影响，并结合市场效率假说对结果进行解读。该方法可直接适用于其他欧洲电力市场。