Quantile Connectedness: Modelling Tail Behaviour in the Topology of Financial Networks

Abstract

We develop a new technique to estimate vector autoregressions by quantile regression. A factor structure is used to remove cross-section correlation in the residuals such that the system can be estimated on an equation-by-equation basis using existing quantile regression toolboxes. We use our model to study credit risk spillovers among a panel of 18 sovereigns and their respective financial sectors between January 2006 and February 2012. We show that idiosyncratic credit risk shocks do not propagate strongly at the median but that powerful spillovers occur in both tails. Furthermore, rolling sample analysis reveals marked time-varying tail-dependence. These important features of credit risk transmission are obscured in models estimated using conventional conditional mean estimators.

Keywords: Network Modelling, Quantile Vector Autoregression with Common Factors, Quantile Connectedness, Financial-Sovereign Credit Risk Transmission, Tail-Dependence

JEL Classification: C31, C32, C58, E44, G01