Abstract
We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information from one stock index to another taking into account different operating hours. Additionally, we apply the formalism of partial correlations to build the dependency network of the data, and calculate the partial Transfer Entropy to quantify the indirect influence that indices have on one another. We find that Transfer Entropy is an effective way to quantify the flow of information between indices, and that a high degree of information flow between indices lagged by one day coincides to same day correlation between them.
Highlights
The world’s financial markets form a complex, dynamic network in which individual markets interact with one another
We show the representation of the correlation and effective Transfer Entropy dependency networks in terms of a distance measure between indices
Since the main aim of this article is to study the networks of indices based on measures that use correlation and Lagged Transfer Entropy, this similarity of structures in time lead us to believe that there is no significant change in the network structure derived using the whole set of data and networks based on particular subsets of data
Summary
The world’s financial markets form a complex, dynamic network in which individual markets interact with one another. Small world or scale-free networks are, in general, more robust to cascades (the propagation of shocks) than random networks, but they are more prone to the propagation of crises if the most central nodes, usually those with more connections, are not themselves backed by sufficient funds Another important finding is that the network structures changed considerably after the crisis of 2008, with a reduction of the number of connected banks and a more robust topology against the propagation of shocks.
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