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Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach

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  • Xisong Jin

    ()

  • Francisco Nadal De Simone

    ()

Abstract

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.

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Bibliographic Info

Paper provided by Central Bank of Luxembourg in its series BCL working papers with number 82.

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Length: 63
Date of creation: Jan 2013
Date of revision:
Handle: RePEc:bcl:bclwop:bclwp082

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Web page: http://www.bcl.lu/

Related research

Keywords: financial stability; procyclicality; macroprudential policy; credit risk; early warning indicators; default probability; non-linearities; generalized dynamic factor model; dynamic copulas; GARCH;

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