DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorChen, Ren_US
dc.creatorWang, Zen_US
dc.creatorYang, Len_US
dc.creatorNg, CTen_US
dc.creatorCheng, TCEen_US
dc.date.accessioned2021-08-20T02:04:30Z-
dc.date.available2021-08-20T02:04:30Z-
dc.identifier.issn0011-7315en_US
dc.identifier.urihttp://hdl.handle.net/10397/90695-
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.subjectCredit Portfolio Risken_US
dc.subjectData Analyticsen_US
dc.subjectDecision Makingen_US
dc.subjectOperational Risk Managementen_US
dc.subjectSimulationen_US
dc.titleA study on operational risk and credit portfolio risk estimation using data analyticsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1111/deci.12473en_US
dcterms.abstractIn this article we consider operational risk and use data analytics to estimate the credit portfolio risk. Specifically, we consider situations in which managers need to make the optimal operational decision on total provision for risk to hedge against the potential risk in the entire supply chain. We build a new structural credit model integrated with data analytics to analyze the joint default risk of credit portfolio. Our model enables the decision maker to better assess the risk of a supply chain, so that they could determine the optimal operational decisions with total provision for risk, and react in a timely manner to economic and environmental changes. We propose an efficient simulation method to estimate the default probability of the credit portfolio with the risk factors having the multivariate t-copula. Moreover, we develop a three-step importance sampling (IS) method for the t-copula credit portfolio risk measurement model to achieve an accurate estimation of the tail probability of the credit portfolio loss distribution. We apply the Levenberg–Marquardt algorithm to estimate the mean-shift vector of the systematic risk factors after the probability measure change. Besides, we empirically examine the changes in the credit portfolio risks of 60 listed Chinese firms in different industries using our proposed method. The results show that our model can help the decision maker make the optimal operational decisions with total provision for risk, which hedges against the potential risk in the entire supply chain.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationDecision sciences, 2021, Early View, https://doi.org/10.1111/deci.12473en_US
dcterms.isPartOfDecision sciencesen_US
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85088294842-
dc.identifier.eissn1540-5915en_US
dc.description.validate202108 bcvcen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1007-n05-
dc.identifier.SubFormID2418-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextG-YBEFen_US
dc.description.pubStatusEarly releaseen_US
dc.date.embargo0000-00-00 (to be updated)en_US
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