European systemic credit risk transmission using Bayesian networks

Laura Ballester, Jesúa López and Jose M. Pavía

Research in International Business and Finance, 2023, vol. 65, issue C

Abstract: The analysis of systemic credit risk is one of the most important concerns within the financial system. Its complexity lies in adequately measuring how the transmission of systemic default spreads through assets or financial markets. The transmission structure of systemic credit risk across several European sectoral CDS is studied by dynamic Bayesian networks. The new approach allows for a more advanced analysis of systemic risk transmission, including long-term and more complex relationships. The modelling reveals as relevant only relationships between the original series and one- and two-lagged series. Network structure learning displays a robust and stationary underlying risk transmission structure, pointing to a consolidated transmission mechanism of systemic credit risk between CDSs. Between 5 % and 40 % of sectoral CDS series variances are explained by the network relationships. The modelling allows us to ascertain which relationships between the CDS series show positive (amplifier) and negative (reducer) effects of systemic risk transmission.

Keywords: CDS; Systemic credit risk; Dynamic Bayesian networks; Vector autoregressive moving average models; Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C45 G15 G32 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:65:y:2023:i:c:s0275531923000405

DOI: 10.1016/j.ribaf.2023.101914

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