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Modeling financial sector joint tail risk in the euro area

Andre Lucas (), Bernd Schwaab and Xin Zhang

No 308, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)

Abstract: We develop a novel high-dimensional non-Gaussian modeling framework to infer measures of conditional and joint default risk 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 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 during 2011–2012, as well as their steep decline after subsequent policy actions.

Keywords: dynamic equicorrelation; generalized hyperbolic distribution; law of large numbers; large portfolio approximation (search for similar items in EconPapers)
JEL-codes: C32 G21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eec, nep-fmk and nep-rmg
Date: 2015-06-01
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Related works:
Journal Article: Modeling Financial Sector Joint Tail Risk in the Euro Area (2017) Downloads
Working Paper: Modeling financial sector joint tail risk in the euro area (2015) Downloads
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