Modelos computacionais e probabilísticos em riscos de crédito
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Tipo
Tese
Data de publicação
2015-02-06
Periódico
Citações (Scopus)
Autores
Barboza, Flavio Luiz de Moraes
Orientador
Basso, Leonardo Fernando Cruz
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Forte, Denis
Sobreiro, Vinicius Amorim
Jucá, Michele Nascimento
Kimura, Herbert
Sobreiro, Vinicius Amorim
Jucá, Michele Nascimento
Kimura, Herbert
Programa
Administração de Empresas
Resumo
This dissertation studies credit risk to promove a discussion about the breadth of scientific
literature and two highlighted topics: regulatory capital and bankruptcy prediction modelling.
These issues are divided among three essays. The first one is a review of literature
in nature. The main studies on credit risk were classified and coded, and a citation-based
approach was used to determine its relevance and contributions. Interesting omissions of
knowledge are found in this work, which give us motivation to develop two subjects. The
second essay discusses the influence of the desirefor higher rating positions for financial
instituitons strategies when aiming to minimize economic capital, considering the borrower s
credit rating and target rating itself. Using a probabilistic distribution model to
simulate loss-given default (LGD), our results show that the use of credit ratings in the
guidance for calculating minimum capital requirements can be an alternative to the banks.
Yet, we find it possible to get better rankings to lend to some small intervals of LGD. The
third study shows a comparative analysis in the performance of computational models,
which are widely used to solve classification problems, and traditional methods applied to
predict failures one year before the event. The models are formulated by machine learning
techniques (support vector machines, bagging, boosting and random forest). Applying
data from U.S. companies from 1985 to 2013, we compare the results of these innovative
methods with neural networks, logistic regression, and discriminant analysis. The major
result of this part of the study is a substantial improvement in predictive power by using
machine learning techniques, when, besides the original variable Z-Score from Altman
(1968), six metrics (or constructs) selected from Carton e Hofer (2006) are included as
explanatory variables. The analysis shows that the bagging and the random forest models
outperform other techniques; all predictions are improved when the suggested constructs
are included in the survey.
Descrição
Palavras-chave
risco de crédito , capital econômico , rating , falência , aprendizagem
de máquinas , credit risk , economic capital , rating , bankruptcy , machine learning
Assuntos Scopus
Citação
BARBOZA, Flavio Luiz de Moraes. Modelos computacionais e probabilísticos em riscos de crédito. 2015. 118 f. Tese (Doutorado em Administração) - Universidade Presbiteriana Mackenzie, São Paulo, 2015.