Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects

ArXiv ID: 2306.13419 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: copulas, intraday electricity markets, probabilistic forecasting, zero-inflated Johnson’s $S_U$ distribution, time-varying dependence

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical methods including copulas, zero-inflated Johnson’s SU distribution, and latent beta regression, indicating high mathematical complexity. It is empirically rigorous with a detailed simulation study on German intraday electricity market data, analyzing cross-product effects and market structure impacts, making it backtest-ready.

  flowchart TD
    A["Research Goal: Multivariate Probabilistic Forecasting for Intraday Power Markets"] --> B["Model Marginals: Zero-Inflated Johnson's S_U Distribution<br>(Market & Fundamental Data)"]
    B --> C["Model Dependence: Copulas with Time-Varying Latent Beta Regression<br>(SIDC & Market Structure)"]
    C --> D["Computational Process: Simulation-based Approach"]
    D --> E["Outcome: Improved Forecasting Accuracy<br>(vs. Univariate Approaches)"]
    E --> F["Outcome: Insights on Market Efficiency & SIDC Impact"]