Regularization Approach for Network Modeling of German Power Derivative Market

TL;DR

This paper introduces a regularization-based network modeling method for the German power derivative market, revealing key risk channels and interdependencies among contracts, especially during peak hours, aiding regulators and investors.

Contribution

It combines high-dimensional variable selection with dynamic network analysis to identify significant risk spillovers in the German power derivative market.

Findings

Identifies main risk contributors and interdependences.

Highlights strong connections between neighboring contracts.

Emphasizes the importance of interdependence during peak hours.

Abstract

In this paper we propose a regularization approach for network modeling of German power derivative market. To deal with the large portfolio, we combine high-dimensional variable selection techniques with dynamic network analysis. The estimated sparse interconnectedness of the full German power derivative market, clearly identify the significant channels of relevant potential risk spillovers. Our empirical findings show the importance of interdependence between different contract types, and identify the main risk contributors. We further observe strong pairwise interconnections between the neighboring contracts especially for the spot contracts trading in the peak hours, its implications for regulators and investors are also discussed. The network analysis of the full German power derivative market helps us to complement a full picture of system risk, and have a better understanding of the German power market functioning and environment.