Spatial Dependence and Data-Driven Networks of International Banks
Ben Craig and
Martin Saldias
No 1627, Working Papers (Old Series) from Federal Reserve Bank of Cleveland
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. Our analysis validates these market-based adjacency matrices as inputs for the spatio-temporal analysis of shocks in the banking system.
Keywords: Network analysis; spatial dependence; banking (search for similar items in EconPapers)
JEL-codes: C21 C23 C45 G21 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2016-12-02
New Economics Papers: this item is included in nep-ifn and nep-ure
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.clevelandfed.org/newsroom-and-events/p ... rnational-banks.aspx Full text (text/html)
Related works:
Working Paper: Spatial Dependence and Data-Driven Networks of International Banks (2016) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:fip:fedcwp:1627
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in Working Papers (Old Series) from Federal Reserve Bank of Cleveland Contact information at EDIRC.
Bibliographic data for series maintained by 4D Library ().