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Systemic risk measurement in banking using self-organizing maps

Author

Listed:
  • James W. Kolari

    (Texas A&M University)

  • Ivan Pastor Sanz

    (Texas A&M University)

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 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.

Suggested Citation

  • James W. Kolari & Ivan Pastor Sanz, 2017. "Systemic risk measurement in banking using self-organizing maps," Journal of Banking Regulation, Palgrave Macmillan, vol. 18(4), pages 338-358, November.
    • Handle: RePEc:pal:jbkreg:v:18:y:2017:i:4:d:10.1057_s41261-016-0002-3
    DOI: 10.1057/s41261-016-0002-3
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    References listed on IDEAS

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    7. Lars Peter Hansen, 2013. "Challenges in Identifying and Measuring Systemic Risk," NBER Chapters, in: Risk Topography: Systemic Risk and Macro Modeling, pages 15-30, National Bureau of Economic Research, Inc.
    8. James Kolari & Michele Caputo & Drew Wagner, 1996. "Trait Recognition: An Alternative Approach to Early Warning Systems in Commercial Banking," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 23(9-10), pages 1415-1434, December.
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    Cited by:

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    2. Sophia Velez & Michael Neubert & Daphne Halkias, 2020. "Banking Finance Experts Consensus on Compliance in US Bank Holding Companies: An e-Delphi Study," JRFM, MDPI, vol. 13(2), pages 1-14, February.
    3. Barthélémy, Sylvain & Binet, Marie-Estelle & Pentecôte, Jean-Sébastien, 2020. "Worldwide economic recoveries from financial crises through the decades," Journal of International Money and Finance, Elsevier, vol. 105(C).
    4. P. K. Viswanathan & Suresh Srinivasan & N. Hariharan, 2020. "Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 19(2), pages 226-261, August.
    5. Savas Papadopoulos & Pantelis Stavroulias & Thomas Sager, 2019. "Systemic early warning systems for EU14 based on the 2008 crisis: proposed estimation and model assessment for classification forecasting," Journal of Banking Regulation, Palgrave Macmillan, vol. 20(3), pages 226-244, September.

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