Spatial Dependence and Data-Driven Networks of International Banks

September 15, 2016

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Format: Chicago

Ben Craig, and Martín Saldías. Spatial Dependence and Data-Driven Networks of International Banks, (USA: International Monetary Fund, 2016) accessed September 18, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

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.

Subject: Banking, Commercial banks, Econometric analysis, Financial institutions, Financial markets, Foreign banks, International banking, Spatial models, Stock markets, Vector autoregression

Keywords: Asia and Pacific, Bank network, Banking, Commercial banks, Core bank, Correlation matrix, Foreign banks, Global, Graph theory, Hierarchical structure, Network analysis, Network structure, Regularization method, Spatial dependence, Spatial models, Stock markets, Time series, Vector autoregression, WP

Publication Details

Pages:

34
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2016/184
Stock No:

WPIEA2016184
ISBN:

9781475536706
ISSN:

1018-5941

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