Multivariate simulation-based forecasting for intraday power markets: Modeling cross-product price effects

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 SU$$ {S}_U $$ distribution with location, scale, and shape parameters that depend on market and fundamental data. The dependence structure is modeled using copulas, accounting for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days and allowing the dependence parameter to be time-varying. We validate our approach in a simulation study for the German intraday electricity market and find that modeling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling 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.