Robust Clustering of EU Banking Data
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In this paper we present an application of robust clustering to the European union (EU) banking system. Banks may differ in several aspects, such as size, business activities and geographical location. After the latest financial crisis, it has become of paramount importance for European regulators to identify common features and issues in the EU banking system and address them in all Member States (or at least those of the Euro area) in a harmonized manner. A key issue is to identify using publicly available information those banks more involved in risky activities, in particular trading, which may need to be restructured to improve the stability of the whole EU banking sector. In this paper we show how robust clustering can help in achieving this purpose. In particular we look for a sound method able to clearly cut the two-dimensional space of trading volumes and their shares over total assets into two subsets, one containing safe banks and the other the risky ones. The dataset, built using banks’ balance sheets, includes 245 banks from all EU27 countries, but Estonia, plus a Norwegian bank. With appropriate parameters, the TCLUST routine could provide better insight of the data and suggest proper thresholds for regulators.
KeywordsEuropean Union Bayesian Information Criterion Total Asset Banking Sector Restriction Factor
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