Abstract
This paper introduces a spectral clustering-based method to show that stock prices contain not only firm but also network-level information. We cluster different stock indices and reconstruct the equity index graph from historical daily closing prices. We show that tail events have a minor effect on the equity index structure. Moreover, covariance and Shannon entropy do not provide enough information about the network. However, Gaussian clusters can explain a substantial part of the total variance. In addition, cluster-wise regressions provide significant and stationer results.
Highlights
The global stock market structure has to be well understood to diversify risk and manage cross-border equity portfolios
Our selection criteria for covered stock indices is based on their classification in the International Monetary Fund (IMF) Economic Outlook 2015, and the MSCI WORLD Index composition in 2015
This study presents a broad analysis of the equity index network structure
Summary
The global stock market structure has to be well understood to diversify risk and manage cross-border equity portfolios. The linear dependence structure of the network is not stable (Erdos et al 2011; Song et al 2011; Maldonado and Anthony 1981). Exogenous shocks have major impact on the correlation structure; uncorrelated assets could start moving together (Heiberger 2014). Correlation-based techniques could cause unwanted variance peaks. Institutional economic surveys (like MSCI 2018) provide qualitatively identified network structures e.g., emerging markets and developed markets to stabilize their classification
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