In this paper, we consider three methods for filtering pertinent information from a series of complex networks modelling the correlations between stock price returns of the DAX 30 stocks for the time period 2001–2012 using the Thomson Reuters Datastream database and also the FNA platform to create the visualizations of the correlation-based networks. These methods reduce the complete 30 × 30 correlation coefficient matrix to a simpler network structure consisting only of the most relevant edges. The chosen network structures include the minimum spanning tree, asset graph and the planar maximally filtered graph. The resulting networks and the extracted information are analysed and compared, looking at the clusters, cliques and connectivity. Finally, we consider some specific time periods (a) a period of crisis (October–December 2008) and (b) a period of recovery (May–August 2010) where we discuss the possible underlying economic reasoning for some aspects of the network structures produced. Overall, we find that network based representations of correlations within a broad market index are useful in providing insights about the growth dynamics of an economy.
- Correlation networks
- DAX 30
- European sovereign-debt crisis