Spatial Dependence and Data-Driven Networks of International Banks

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. Our analysis validates these market-based adjacency matrices as inputs for the spatio-temporal analysis of shocks in the banking system.