# 13-063/IV/DSF56 (2013-05-13; 2014-10-13)

Andre Lucas, VU University Amsterdam; Bernd Schwaab, European Central Bank, Financial Markets Research; Xin Zhang, VU University Amsterdam, and Sveriges Riksbank, Research Division
systemic risk; dynamic equicorrelation model; generalized hyperbolic distribution; Law of Large Numbers
JEL codes:
G21, C32

Forthcoming in the 'Journal of Applied Econometrics'.

We develop a novel high-dimensional non-Gaussian modeling framework to infer conditional and joint risk measures for many financial sector firms. The model is based on a dynamic Generalized Hyperbolic Skewed-t block-equicorrelation copula with time-varying volatility and dependence parameters that naturally accommodates asymmetries, heavy tails, as well as non-linear and time-varying default dependence. We demonstrate how to apply a conditional law of large numbers in this setting to define risk measures that can be evaluated quickly and reliably. We apply the modeling framework to assess the joint risk from multiple financial firm defaults in the euro area during the 2008-2012 financial and sovereign debt crisis. We document unprecedented tail risks during 2011-12, as well as their steep decline after subsequent policy actions.