Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach
Xisong Jin () and
Francisco Nadal De Simone ()
No 82, BCL working papers from Central Bank of Luxembourg
This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology of Segoviano, and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks? default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in three forms: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks and; (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust outof-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.
Keywords: financial stability; procyclicality, macroprudential policy; credit risk; early warning indicators; default probability, non-linearities, generalized dynamic factor model; dynamic copulas; GARCH (search for similar items in EconPapers)
JEL-codes: C30 E44 G1 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban, nep-cba and nep-rmg
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Journal Article: Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:bcl:bclwop:bclwp082
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