The use of data reduction techniques to assess systemic risk: An application to the Chilean banking system

Abstract

In recent years, data reduction techniques have gained scene in applied economics. Following this trail, we apply and extend the use of a data reduction technique such as principal component analysis (PCA), to assess the degree of systemic risk in the Chilean banking system. In particular, we address the following questions: (i) to what extent the degree of common risk exposure in the banking system has changed over the past decades; (ii) during which periods this exposure increased the most; and (iii) based on predefined thresholds, when this commonality has become systemically relevant. Additionally, we identify systemically important financial institutions (SIFIs) based on their contribution to the degree of common risk exposure during periods of higher systemic risk. We find that prior to the 2008-09 global financial crisis the degree of common risk exposure in Chile increased significantly, and that the banks that contributed the most were not necessarily the biggest ones in size, as measured by their assets share.