Spatial Dependence and Data-Driven Networks of International Banks

Ben Craig and Martin Saldias

No 2016/184, IMF Working Papers from International Monetary Fund

Abstract: This paper computes data-driven correlation networks based on the stock returns of international banks and conducts a comprehensive analysis of their topological properties. We first apply spatial-dependence methods to filter the effects of strong common factors and a thresholding procedure to select the significant bilateral correlations. The analysis of topological characteristics of the resulting correlation networks shows many common features that have been documented in the recent literature but were obtained with private information on banks' exposures, including rich and hierarchical structures, based on but not limited to geographical proximity, small world features, regional homophily, and a core-periphery structure.

Keywords: WP; correlation matrix; hierarchical structure; graph theory; Network analysis; spatial dependence; banking; core bank; time series; bank network; regularization method; network structure; Spatial models; Vector autoregression; Foreign banks; Stock markets; Commercial banks; Global; Asia and Pacific (search for similar items in EconPapers)
Pages: 34
Date: 2016-09-15
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Citations: View citations in EconPapers (2)

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Working Paper: Spatial Dependence and Data-Driven Networks of International Banks (2016)
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