Systemic risk measurement in banking using self-organizing maps Academic Article

abstract

• 2016 Macmillan Publishers Ltd. 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 to generate annual 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 management are discussed.

authors

• Kolari, James

published proceedings

• JOURNAL OF BANKING REGULATION

author list (cited authors)

• Kolari, J. W., & Pastor Sanz, I.

citation count

• 8

complete list of authors

• Kolari, James W||Pastor Sanz, Ivan

publication date

• November 2017

publisher

• Springer Nature  Publisher

published in

• n1745-6452ISSN  Journal

keywords

• Bank Condition
• Bank Systemic Risk
• Self-organizing Maps
• Trait Recognition

Digital Object Identifier (DOI)

• 10.1057/s41261-016-0002-3

start page

• 338

end page

• 358

volume

• 18

issue

• 4

URL

• http://dx.doi.org/10.1057/s41261-016-0002-3