Spatial dependence in credit risk and its improvement in credit scoring

Página do item simplificado
dc.contributor.authorRINALDO ARTES
dc.contributor.authorFernandes, Guilherme Barreto
dc.coverage.paisNão Informadopt_BR
dc.creatorFernandes, Guilherme Barreto
dc.date.accessioned2022-08-15T14:50:12Z
dc.date.available2022-08-15T14:50:12Z
dc.date.issued2016
dc.description.notesTexto completopt_BR
dc.description.otherCredit scoring models are important tools in the credit granting process. These models measure the credit risk of a prospective client based on idiosyncratic variables and macroeconomic factors. However, small and medium sized enterprises (SMEs) are subject to the effects of the local economy. From a data set with the localization and default information of 9 million Brazilian SMEs, provided by Serasa Experian (the largest Brazilian credit bureau), we propose a measure of the local risk of default based on the application of ordinary kriging. This variable has been included in logistic credit scoring models as an explanatory variable. These models have shown better performance when compared to models without this variable. A gain around 7 percentage points of KS and Gini was observed.pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doihttp://dx.doi.org/10.1016/j.ejor.2015.07.013pt_BR
dc.identifier.issn3772217pt_BR
dc.identifier.issue249pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/3989
dc.language.isoInglêspt_BR
dc.publisherElsevierpt_BR
dc.relation.ispartofEuropean Journal of Operational Researchpt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORpt_BR
dc.subject.keywordsRisk analysispt_BR
dc.subject.keywordsSpatial dependencept_BR
dc.subject.keywordsSME credit riskpt_BR
dc.subject.keywordsCredit scoringpt_BR
dc.subject.keywordsOrdinary krigingpt_BR
dc.titleSpatial dependence in credit risk and its improvement in credit scoringpt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.sciencedirect.com/science/article/pii/S0377221715006463
local.subject.cnpqCiências Exatas e da Terrapt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication8b791c94-f3e5-4e04-af26-594195a8f576
relation.isAuthorOfPublication.latestForDiscovery8b791c94-f3e5-4e04-af26-594195a8f576
Arquivos
Pacote Original
Agora exibindo 1 - 1 de 1
N/D
Nome:
R_Artigo_2016_A comparison of strategies to develop_tc.pdf
Tamanho:
308.3 KB
Formato:
Adobe Portable Document Format
Descrição:
R_Artigo_2016_A comparison of strategies to develop_tc
Baixar
Licença do Pacote
Agora exibindo 1 - 1 de 1
N/D
Nome:
license.txt
Tamanho:
282 B
Formato:
Item-specific license agreed upon to submission
Descrição:
Baixar
Coleções
Coleção de Artigos Acadêmicos