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Visual Macroprudential Surveillance of Banks

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  • Peter Sarlin

Abstract

We create a tool for visual surveillance of the European banking system from a macroprudential perspective. The tool performs visual dynamic clustering with the self‐organizing time map (SOTM) to visualize evolving multivariate data from two viewpoints: (i) multivariate cluster structures, and (ii) univariate drivers of changes in structures. In assessing the European banking system, the main tasks the SOTM can be used for are (i) identifying structural changes and breaking points in a large number of risk indicators, and their specific location in the cross‐section, and (ii) identifying the build‐up of, or generally changes in, individual risk indicators in the banking system as a whole. While the former view provides indications of changes in the banking system, the latter describes the sources of these changes. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Peter Sarlin, 2016. "Visual Macroprudential Surveillance of Banks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 257-264, October.
    • Handle: RePEc:wly:isacfm:v:23:y:2016:i:4:p:257-264
    DOI: 10.1002/isaf.1391
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    References listed on IDEAS

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    1. Claudio Borio, 2011. "Implementing the Macroprudential Approach to Financial Regulation and Supervision," Chapters, in: Christopher J. Green & Eric J. Pentecost & Tom Weyman-Jones (ed.), The Financial Crisis and the Regulation of Finance, chapter 7, Edward Elgar Publishing.
    2. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
    3. Alessi, Lucia & Detken, Carsten, 2011. "Quasi real time early warning indicators for costly asset price boom/bust cycles: A role for global liquidity," European Journal of Political Economy, Elsevier, vol. 27(3), pages 520-533, September.
    4. Betz, Frank & Oprică, Silviu & Peltonen, Tuomas A. & Sarlin, Peter, 2014. "Predicting distress in European banks," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 225-241.
    5. Langone, Rocco & Alzate, Carlos & Suykens, Johan A.K., 2013. "Kernel spectral clustering with memory effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2588-2606.
    6. Jaume Puig-Forne & Mr. Ken Miyajima & Rebecca McCaughrin & Mr. Peter Dattels, 2010. "Can You Map Global Financial Stability?," IMF Working Papers 2010/145, International Monetary Fund.
    7. Castrén, Olli & Fitzpatrick, Trevor & Sydow, Matthias, 2009. "Assessing portfolio credit risk changes in a sample of EU large and complex banking groups in reaction to macroeconomic shocks," Working Paper Series 1002, European Central Bank.
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