Spatial dependence and data-driven networks of international banks /

Spatial dependence and data-driven networks of international banks /

Saved in:

Bibliographic Details
Author / Creator:	Craig, Ben R., author.
Imprint:	[Washington, D.C.] : International Monetary Fund, [2016] ©2016
Description:	1 online resource (34 pages) : color illustrations
Language:	English
Series:	IMF working paper ; WP/16/184 IMF working paper ; WP/16/184.
Subject:	Banks and banking, International -- Econometric models. Financial risk management -- Econometric models. Banks and banking, International -- Econometric models.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12507619

Hidden Bibliographic Details
Other authors / contributors:	Salidas, Martin, author, (IMF staff) International Monetary Fund, publisher. International Monetary Fund. Monetary and Capital Markets Department, issuing body.
ISBN:	9781475536706 1475536704 9781475537840 1475537840
Notes:	"September 2016." At head of title: International Monetary Fund, Monetary and Capital Markets Department. Includes bibliographical references. Online resource; title from pdf title page (IMF.org website, viewed October 17, 2016).
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.
Other form:	Print Version: Craig, Ben. Spatial Dependence and Data-Driven Networks of International Banks. Washington, D.C. : International Monetary Fund,2016 9781475536706
Standard no.:	10.5089/9781475536706.001

Description
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.
Item Description:	"September 2016." At head of title: International Monetary Fund, Monetary and Capital Markets Department.
Physical Description:	1 online resource (34 pages) : color illustrations
Bibliography:	Includes bibliographical references.
ISBN:	9781475536706 1475536704 9781475537840 1475537840