A System-Wide Approach to Measure Connectivity in the Financial Sector

Abstract

We introduce and estimate a model that leverages a system-wide approach to identify systemically important financial institutions. It is based on a recently developed Lasso penalized Vector Auto-regressive (LVAR) model, that exhibits desirable statistical properties and enables us to detect systemic events and key institutions associated with them. The model explicitly allows for the possibility of connectivity amongst all institutions under consideration: this is in sharp contrast with extant measures of systemic risk that, either explicitly or implicitly, estimate such connections using pair-wise relationships between institutions. Using simulations we show that our approach can provide considerable improvement over extant measures in detecting systemically important institutions. Finally, we estimate our model for large financial institutions in the U.S. and show its usefulness in detecting systemically stressful periods and institution with real data.

Keywords: Systemic Risk, Financial Networks, Lasso, Vector Autoregression

JEL Classification: C58, G01, G29