Spatial Dependence and Data-Driven Networks of International Banks

Author/Editor:

Ben Craig ; Martín Saldías

Publication Date:

September 15, 2016

Electronic Access:

Free Download. Use the free Adobe Acrobat Reader to view this PDF file

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.

Series:

Working Paper No. 2016/184

Subject:

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

English

Publication Date:

September 15, 2016

ISBN/ISSN:

9781475536706/1018-5941

Stock No:

WPIEA2016184

Pages:

34

Please address any questions about this title to publications@imf.org