<div class="csl-bib-body">
<div class="csl-entry">Blaskó, P. (2019). <i>Identification of credit default drivers via lasso estimation in the logistic regression model</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.66160</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2020.66160
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/15075
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dc.description.abstract
In this work, a binary logistic regression model for two-year default probabilities has been estimated on a data set containing information on 150.000 clients available on kaggle's competition "GiveMeSomeCredit". The optimal model has been selected by choosing a subset of continuous, categorical and ordinal variables reflecting sociodemographic and behavioral properties of the client as well as characteristics of their loans using the Lasso estimator. The issue of non-linear dependence of default probabilities on the regressors has been tackled by discretization of regressors using a version of the fused Lasso in a multivariate environment. We find that the model provides an excellent fit of the data by reaching an average out-of-sample AUC of over 86%, independent of the model selection criterion (AIC, BIC or CV). This value lies in the upper range of the industry standard and in range of more complicated modeling approaches such as in Wang et al. (2015). We see that the estimator gives the strongest weights to behavioral variables such as past due status and limit utilization, while sociodemographic variables and loan properties are much less significant.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
credit default
en
dc.subject
Lasso
en
dc.subject
logistic regression
en
dc.title
Identification of credit default drivers via lasso estimation in the logistic regression model
en
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2020.66160
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Péter Blaskó
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E105 - Institut für Stochastik und Wirtschaftsmathematik
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC15676004
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dc.description.numberOfPages
65
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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item.openaccessfulltext
Open Access
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item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
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item.grantfulltext
open
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item.mimetype
application/pdf
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item.languageiso639-1
en
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item.openairetype
master thesis
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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crisitem.author.dept
E105 - Institut für Stochastik und Wirtschaftsmathematik