Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | https://hdl.handle.net/10438/2686 |
Resumo: | Credit risk management has assumed increasing importance for the managers and directors of enterprises. Thus, different approaches aimed to measure the probability of default are under discussion nowadays. This paper evaluates models that have become more popular over the last 30 years in order forecast defaults or to provide information regarding to financial difficulties of enterprises. This paper will focus on the KMV model in order to estimate the probability of default, its methodology based on market value of the asset and its volatility and finally estimate the probability of default. Finally, to test the KMV model will be used a sample of global steel companies that have credit in Companhia Vale do Rio Doce (CVRD), which will allow us to make comparisons with the models presented in this work. |
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Moura, João SichieriEscolas::EPGEFGVLa Rocque, Eduarda Cunha deAragão, César Santiago Lima de2009-07-07T14:51:00Z2009-07-07T14:51:00Z2007-05-30MOURA, João Sichieri. Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico. Dissertação (Mestrado em Finanças e Economia Empresarial) - Escola de Pós-Graduação em Economia, Fundação Getúlio Vargas - FGV, Rio de Janeiro, 2007.https://hdl.handle.net/10438/2686Credit risk management has assumed increasing importance for the managers and directors of enterprises. Thus, different approaches aimed to measure the probability of default are under discussion nowadays. This paper evaluates models that have become more popular over the last 30 years in order forecast defaults or to provide information regarding to financial difficulties of enterprises. This paper will focus on the KMV model in order to estimate the probability of default, its methodology based on market value of the asset and its volatility and finally estimate the probability of default. Finally, to test the KMV model will be used a sample of global steel companies that have credit in Companhia Vale do Rio Doce (CVRD), which will allow us to make comparisons with the models presented in this work.A gestão de riscos de crédito tem assumido importância cada vez maior para os gestores e administradores de empresas. Desta forma, diferentes abordagens voltadas para medir a capacidade de pagamento de empresas hoje estão em discussão. Este trabalho avalia modelos que se tornaram mais populares nos últimos 30 anos com o intuito de prever falências ou dificuldades financeiras de empresas. Será dado um enfoque para o modelo KMV de estimação de probabilidade de default e sua metodologia baseada no valor de mercado do ativo e sua volatilidade para estimar a probabilidade de default. Por fim, para testar o modelo KMV, será utilizada uma amostra de empresas siderúrgicas mundiais que possuem crédito na Companhia Vale do Rio Doce (CVRD), que nos permitirá efetuar comparações com os modelos apresentados neste trabalho.porJoão MouraModelo KMVCredit riskSteel industryEconomiaAdministração de riscoAdministração de créditoRisco (Economia)CréditosAvaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgicoinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVORIGINALTese_MFEE_JOAO SICHIERI_2007.pdfTese_MFEE_JOAO SICHIERI_2007.pdfPDFapplication/pdf336631https://repositorio.fgv.br/bitstreams/5ce53151-e459-481d-b3ed-e5bebd7cdf55/downloaddc4d29066af0bd6c7564db0c209c3eb2MD51LICENSElicense.txtlicense.txttext/plain; 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|
dc.title.por.fl_str_mv |
Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico |
title |
Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico |
spellingShingle |
Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico Moura, João Sichieri Modelo KMV Credit risk Steel industry Economia Administração de risco Administração de crédito Risco (Economia) Créditos |
title_short |
Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico |
title_full |
Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico |
title_fullStr |
Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico |
title_full_unstemmed |
Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico |
title_sort |
Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico |
author |
Moura, João Sichieri |
author_facet |
Moura, João Sichieri |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EPGE |
dc.contributor.affiliation.none.fl_str_mv |
FGV |
dc.contributor.member.none.fl_str_mv |
La Rocque, Eduarda Cunha de |
dc.contributor.author.fl_str_mv |
Moura, João Sichieri |
dc.contributor.advisor1.fl_str_mv |
Aragão, César Santiago Lima de |
contributor_str_mv |
Aragão, César Santiago Lima de |
dc.subject.por.fl_str_mv |
Modelo KMV |
topic |
Modelo KMV Credit risk Steel industry Economia Administração de risco Administração de crédito Risco (Economia) Créditos |
dc.subject.eng.fl_str_mv |
Credit risk Steel industry |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Administração de risco Administração de crédito Risco (Economia) Créditos |
description |
Credit risk management has assumed increasing importance for the managers and directors of enterprises. Thus, different approaches aimed to measure the probability of default are under discussion nowadays. This paper evaluates models that have become more popular over the last 30 years in order forecast defaults or to provide information regarding to financial difficulties of enterprises. This paper will focus on the KMV model in order to estimate the probability of default, its methodology based on market value of the asset and its volatility and finally estimate the probability of default. Finally, to test the KMV model will be used a sample of global steel companies that have credit in Companhia Vale do Rio Doce (CVRD), which will allow us to make comparisons with the models presented in this work. |
publishDate |
2007 |
dc.date.issued.fl_str_mv |
2007-05-30 |
dc.date.accessioned.fl_str_mv |
2009-07-07T14:51:00Z |
dc.date.available.fl_str_mv |
2009-07-07T14:51:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
MOURA, João Sichieri. Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico. Dissertação (Mestrado em Finanças e Economia Empresarial) - Escola de Pós-Graduação em Economia, Fundação Getúlio Vargas - FGV, Rio de Janeiro, 2007. |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10438/2686 |
identifier_str_mv |
MOURA, João Sichieri. Avaliação do risco de crédito: aplicação do modelo KMV para obter a probabilidade de default no setor siderúrgico. Dissertação (Mestrado em Finanças e Economia Empresarial) - Escola de Pós-Graduação em Economia, Fundação Getúlio Vargas - FGV, Rio de Janeiro, 2007. |
url |
https://hdl.handle.net/10438/2686 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
João Moura |
publisher.none.fl_str_mv |
João Moura |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do FGV (FGV Repositório Digital) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
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Fundação Getulio Vargas (FGV) |
instacron_str |
FGV |
institution |
FGV |
reponame_str |
Repositório Institucional do FGV (FGV Repositório Digital) |
collection |
Repositório Institucional do FGV (FGV Repositório Digital) |
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https://repositorio.fgv.br/bitstreams/5ce53151-e459-481d-b3ed-e5bebd7cdf55/download https://repositorio.fgv.br/bitstreams/2b02fcad-5b2d-42e4-90e2-117978098c4b/download https://repositorio.fgv.br/bitstreams/a7612ec7-3341-4302-83dd-d1b414d8ae20/download https://repositorio.fgv.br/bitstreams/40802f3a-62c6-4d98-9e6e-86515e30ed18/download |
bitstream.checksum.fl_str_mv |
dc4d29066af0bd6c7564db0c209c3eb2 2b4a8f5512bf77ceca4e746aa0d20442 e2b25c2ca5472b3561a55477976fec26 e24eb56c595b753cccc76e113f3f4e55 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV) |
repository.mail.fl_str_mv |
|
_version_ |
1813797846270345216 |