Modeling financial sector joint tail risk in the euro area - André Lucas; Bernd Schwaab; Xin Zhang (European Central Bank (ECB), 2015)
We develop a novel high-dimensional non-Gaussian modeling framework to infer measures of conditional and joint default risk for numerous 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 apply a conditional law of large numbers in this setting to define joint and conditional risk measures that can be evaluated quickly and reliably. We apply the modeling framework to assess the joint risk from multiple defaults in the euro area during the 2008-2012 financial and sovereign debt crisis. We document unprecedented tail risks between 2011-2012, as well as their steep decline following subsequent policy actions.
Keywords: G21; C32; 330; dynamic equicorrelation; generalized hyperbolic distribution; large portfolio approximation; law of large numbers