Abstract
Over the last 2 decades, financial systems have been studied and analyzed from the perspective of complex networks, where the nodes and edges in the network represent the various financial components and the strengths of correlations between them. Here, we adopt a similar network-based approach to analyze the daily closing prices of 69 global financial market indices across 65 countries over a period of 2000–2014. We study the correlations among the indices by constructing threshold networks superimposed over minimum spanning trees at different time frames. We investigate the effect of critical events in financial markets (crashes and bubbles) on the interactions among the indices by performing both static and dynamic analyses of the correlations. We compare and contrast the structures of these networks during periods of crashes and bubbles, with respect to the normal periods in the market. In addition, we study the temporal evolution of traditional market indicators, various global network measures, and the recently developed edge-based curvature measures. We show that network-centric measures can be extremely useful in monitoring the fragility in the global financial market indices.
Highlights
It is possible to describe a financial market using the framework of complex networks such that the nodes in a network represent the financial components and an edge between any two components indicates an interaction between them
In the present study of the global market indices, the novelty lies in the following: i) Construction of the threshold network Sτ(t), as superposition of the minimum spanning tree (MST) of the cross-correlation matrix and the network of edges with correlations Ciτj ≥ 0.65, which ensures that each threshold network is a connected graph and captures the most relevant edges between market indices
In Supplementary Material, we have reported the results for networks constructed using MST and two other threshold values, i.e., Ciτj ≥ 0.75 and Ciτj ≥ 0.85
Summary
It is possible to describe a financial market using the framework of complex networks such that the nodes in a network represent the financial components and an edge between any two components indicates an interaction between them. A growing amount of research is focused on methods devised to extract relevant correlations from the correlation matrix and study the topological, hierarchical, and clustering properties of the resulting networks. Dynamic asset trees, introduced by Onnela et al [3, 4], were analyzed to monitor the evolution of financial stock markets using the hierarchical clustering properties of such trees. Boginski et al [5] constructed threshold networks by extracting the edges with correlation values exceeding a chosen threshold and analyzed degree distribution, cliques, and independent sets on the threshold network. Tumminello et al [6] introduced planar maximally filtered graph (PMFG) as a tool to extract important edges from the correlation matrix, which contains more information than the MST, while preserving the hierarchical structure induced by Global Financial Indices Network Indicators
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