Systemic Risk Measurement in Banking Using Self-Organizing Maps

50 Pages Posted: 8 Nov 2014 Last revised: 30 Sep 2015

See all articles by James W. Kolari

James W. Kolari

Texas A&M University - Department of Finance

Ivan Sanz

University of Valladolid - Faculty of Economic Science and Business Studies

Date Written: September 29, 2015

Abstract

This paper utilizes neural network mapping technology to assess the dynamic nature of systemic risk over time in the banking industry. We combine the nonparametric method of trait recognition with self-organizing maps (SOMs) to generate yearly pictures of the 16 largest U.S. banks’ financial condition from 2003 to 2012. Results show that systemic risk was gradually rising prior to the 2008-2009 financial crisis and peaked in 2009. Thereafter big banks were recovering but considerable systemic risk lingered. Implications to bank regulatory policy and credit risk measurement are discussed.

Keywords: Self-organizing maps, systemic risk, trait recognition, bank condition

JEL Classification: C14, C53, G21, G28

Suggested Citation

Kolari, James W. and Sanz, Ivan, Systemic Risk Measurement in Banking Using Self-Organizing Maps (September 29, 2015). Available at SSRN: https://ssrn.com/abstract=2520249 or http://dx.doi.org/10.2139/ssrn.2520249

James W. Kolari (Contact Author)

Texas A&M University - Department of Finance ( email )

MS-4218
Department of Finance
College Station, TX TX 77843-4218
United States
979-845-4803 (Phone)
979-845-3884 (Fax)

Ivan Sanz

University of Valladolid - Faculty of Economic Science and Business Studies ( email )

Avenida Valle Esgueva, 6
Valladolid
Spain

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