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European Systemic Credit Risk Transmission Using Dynamic Bayesian Networks.

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European Systemic Credit Risk Transmission Using Dynamic Bayesian Networks.

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dc.contributor.author Ballester Miquel, Laura
dc.contributor.author López Máñez, Jesúa
dc.contributor.author Pavía Miralles, José Manuel
dc.date.accessioned 2023-04-19T13:49:22Z
dc.date.available 2023-04-19T13:49:22Z
dc.date.issued 2023
dc.identifier.citation Ballester Miquel, Laura López Máñez, Jesúa Pavía Miralles, José Manuel 2023 European Systemic Credit Risk Transmission Using Dynamic Bayesian Networks. Research In International Business And Finance 65 101914
dc.identifier.uri https://hdl.handle.net/10550/86116
dc.description.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 twolagged 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.
dc.language.iso eng
dc.relation.ispartof Research In International Business And Finance, 2023, vol. 65, num. 101914
dc.subject Avaluació del risc
dc.title European Systemic Credit Risk Transmission Using Dynamic Bayesian Networks.
dc.type journal article
dc.date.updated 2023-04-19T13:49:23Z
dc.identifier.doi 10.1016/j.ribaf.2023.101914
dc.identifier.idgrec 157377
dc.rights.accessRights open access

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