We propose a novel approach based on the Marshall-Olkin (MO) copula to estimate the impact of systematic and idiosyncratic components on cross-border systemic risk. To use the data on non-failed banks in the suggested method, we consider the time to bank failure as a censored variable. Therefore, we propose a pseudo-maximum likelihood estimation procedure for the MO copula for a Type I censored sample. We derive the log-likelihood function, the copula parameter estimator and the bootstrap confidence intervals. Empirical data on the banking system of three European countries (Germany, Italy and the UK) shows that the proposed censored model can accurately estimate the systematic component of cross-border systemic risk. (C) 2019 Elsevier B.V. All rights reserved.

Calabrese, R., Osmetti, S. A., A new approach to measure systemic risk: A bivariate copula model for dependent censored data, <<EUROPEAN JOURNAL OF OPERATIONAL RESEARCH>>, 2019; 279 (3): 1053-1064. [doi:10.1016/j.ejor.2019.06.027] [http://hdl.handle.net/10807/143720]

A new approach to measure systemic risk: A bivariate copula model for dependent censored data

Osmetti, Silvia Angela
2019

Abstract

We propose a novel approach based on the Marshall-Olkin (MO) copula to estimate the impact of systematic and idiosyncratic components on cross-border systemic risk. To use the data on non-failed banks in the suggested method, we consider the time to bank failure as a censored variable. Therefore, we propose a pseudo-maximum likelihood estimation procedure for the MO copula for a Type I censored sample. We derive the log-likelihood function, the copula parameter estimator and the bootstrap confidence intervals. Empirical data on the banking system of three European countries (Germany, Italy and the UK) shows that the proposed censored model can accurately estimate the systematic component of cross-border systemic risk. (C) 2019 Elsevier B.V. All rights reserved.
2019
Inglese
Calabrese, R., Osmetti, S. A., A new approach to measure systemic risk: A bivariate copula model for dependent censored data, <<EUROPEAN JOURNAL OF OPERATIONAL RESEARCH>>, 2019; 279 (3): 1053-1064. [doi:10.1016/j.ejor.2019.06.027] [http://hdl.handle.net/10807/143720]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/143720
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 18
social impact