  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.
