Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/31316 |
Resumo: | The objective of this thesis is to analyze whether the default measures obtained through the standard EBIT-based structural model are comparable to those obtained through credit ratings. This study covers all non-financial companies present on the S&P500 throughout the 2004-2018 period. Credit risk measures coming from the two approaches were found to be broadly comparable. Nevertheless, it was found that on average the structural model under predicts the credit-ratings probability of default by 0,68 p.p. and over predicts the distance to default by 0,57 standard deviations. This under prediction of credit risk was observed across all sectors, though with different degrees of intensity depending on the economic sector. The underprediction was found in all years of study except the financial crisis period. This dissertation proceeded by analysing the relation between the model and rating agencies default measures. The two estimates show a relatively strong correlation, notably 44% in the case of the probability of default and 52% in the case of the distance to default. The relation between the distances to default measures has been further studied through panel data regressions both on levels (with and without firm fixed effects) and on time differences. Under all approaches the coefficient for the model distance to default measure was found to be relatively small but significant at all the usual confidence levels. This result suggests that the structural model tends to overreact on all new information, while rating agencies act more smoothly. |
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Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firmsCredit riskDefault predictionStructural modelsCredit ratingsRisco de créditoPrevisão de insolvênciaModelos estruturaisClassificações de créditoDomínio/Área Científica::Ciências Sociais::Economia e GestãoThe objective of this thesis is to analyze whether the default measures obtained through the standard EBIT-based structural model are comparable to those obtained through credit ratings. This study covers all non-financial companies present on the S&P500 throughout the 2004-2018 period. Credit risk measures coming from the two approaches were found to be broadly comparable. Nevertheless, it was found that on average the structural model under predicts the credit-ratings probability of default by 0,68 p.p. and over predicts the distance to default by 0,57 standard deviations. This under prediction of credit risk was observed across all sectors, though with different degrees of intensity depending on the economic sector. The underprediction was found in all years of study except the financial crisis period. This dissertation proceeded by analysing the relation between the model and rating agencies default measures. The two estimates show a relatively strong correlation, notably 44% in the case of the probability of default and 52% in the case of the distance to default. The relation between the distances to default measures has been further studied through panel data regressions both on levels (with and without firm fixed effects) and on time differences. Under all approaches the coefficient for the model distance to default measure was found to be relatively small but significant at all the usual confidence levels. This result suggests that the structural model tends to overreact on all new information, while rating agencies act more smoothly.Esta tese tem como objetivo comparar a probabilidade de insolvência obtida através de um modelo estrutural baseado no EBIT da empresa e o resultante das classificações das agências de rating. Este estudo cobre todas as instituições não financeiras, inteiramente presentes no S&P500 durante o período de 2004 a 2018. As duas medidas de risco de crédito são grosso modo comparáveis. Contudo, concluiu-se que, em média, o modelo estrutural subestima as probabilidades de insolvência atribuídas pelas agências em 0.68 p.p. e sobrestima a distância à insolvência em 0.57 desvios-padrão. Esta subestimação do risco de crédito foi observada ao longo de todos os setores, ainda que com diferentes graus de intensidade. A subestimação ocorreu em todos os anos, com exceção do período da crise financeira. Esta dissertação analisou também a relação temporal entre o modelo e as medidas de insolvência provenientes de instituições de classificações de crédito. As duas estimativas mostram uma correlação relativamente forte, nomeadamente 44% para probabilidade de insolvência e de 52% para a distância à insolvência. A relação entre as medidas de distância à insolvência foi analisada através de regressões com dados em painel, em níveis (com e sem efeitos fixos da empresa) e em diferenças temporais. Em todas as abordagens, o coeficiente para o modelo da medida distência à insolvência mostrou-se relativamente pequeno, mas significativo a todos os níveis de confiança, sugerindo que o modelo estrutural tende a exagerar toda a informação nova, em contraponto com as agências de classificação de crédito que agem de forma mais gradual.Silva, NunoVeritati - Repositório Institucional da Universidade Católica PortuguesaMadsen, Simen Bjølseth2020-11-10T10:31:19Z2020-07-0120202020-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/31316TID:202517705enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-12T17:36:50Zoai:repositorio.ucp.pt:10400.14/31316Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:25:13.581750Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms |
title |
Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms |
spellingShingle |
Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms Madsen, Simen Bjølseth Credit risk Default prediction Structural models Credit ratings Risco de crédito Previsão de insolvência Modelos estruturais Classificações de crédito Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms |
title_full |
Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms |
title_fullStr |
Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms |
title_full_unstemmed |
Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms |
title_sort |
Can the standard EBIT-based structural model replicate credit ratings? : an empirical study on S&P500 non-financial firms |
author |
Madsen, Simen Bjølseth |
author_facet |
Madsen, Simen Bjølseth |
author_role |
author |
dc.contributor.none.fl_str_mv |
Silva, Nuno Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Madsen, Simen Bjølseth |
dc.subject.por.fl_str_mv |
Credit risk Default prediction Structural models Credit ratings Risco de crédito Previsão de insolvência Modelos estruturais Classificações de crédito Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Credit risk Default prediction Structural models Credit ratings Risco de crédito Previsão de insolvência Modelos estruturais Classificações de crédito Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
The objective of this thesis is to analyze whether the default measures obtained through the standard EBIT-based structural model are comparable to those obtained through credit ratings. This study covers all non-financial companies present on the S&P500 throughout the 2004-2018 period. Credit risk measures coming from the two approaches were found to be broadly comparable. Nevertheless, it was found that on average the structural model under predicts the credit-ratings probability of default by 0,68 p.p. and over predicts the distance to default by 0,57 standard deviations. This under prediction of credit risk was observed across all sectors, though with different degrees of intensity depending on the economic sector. The underprediction was found in all years of study except the financial crisis period. This dissertation proceeded by analysing the relation between the model and rating agencies default measures. The two estimates show a relatively strong correlation, notably 44% in the case of the probability of default and 52% in the case of the distance to default. The relation between the distances to default measures has been further studied through panel data regressions both on levels (with and without firm fixed effects) and on time differences. Under all approaches the coefficient for the model distance to default measure was found to be relatively small but significant at all the usual confidence levels. This result suggests that the structural model tends to overreact on all new information, while rating agencies act more smoothly. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-10T10:31:19Z 2020-07-01 2020 2020-07-01T00:00:00Z |
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.uri.fl_str_mv |
http://hdl.handle.net/10400.14/31316 TID:202517705 |
url |
http://hdl.handle.net/10400.14/31316 |
identifier_str_mv |
TID:202517705 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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