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.
|Peer-reviewed scientific journal||Intelligent Systems in Accounting, Finance and Management|
|Number of pages||8|
|Publication status||Published - 2016|
|MoE publication type||A1 Journal article - refereed|
- 511 Economics