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Statements

Subject Item
n2:RIV%2F61989100%3A27510%2F13%3A86082949%21RIV14-MSM-27510___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
This paper is devoted to the estimation of the probability of default (PD) as a crucial parameter in risk management, requests for loans, rating estimation, pricing of credit derivatives and many others key financial fields. Particularly, in this paper we will estimate the PD of US banks by means of the statistical models, generally known as credit scoring models. First, in theoretical part, we will briefly introduce the two main categories of credit scoring models, which will be afterwards used in application part – linear discriminant analysis and regression models (logit and probit), including testing the statistical significance of estimated parameters. In the main part of the paper we will work with the sample of almost three hundred U.S. commercial banks which will be separate into two groups (non-default and default) on the basis of historical information. Subsequently, we will stepwise apply the mentioned above scoring models on this sample to derive several models for estimation of PD. Further we will apply these models to the control sample to determine the most appropriate model. This paper is devoted to the estimation of the probability of default (PD) as a crucial parameter in risk management, requests for loans, rating estimation, pricing of credit derivatives and many others key financial fields. Particularly, in this paper we will estimate the PD of US banks by means of the statistical models, generally known as credit scoring models. First, in theoretical part, we will briefly introduce the two main categories of credit scoring models, which will be afterwards used in application part – linear discriminant analysis and regression models (logit and probit), including testing the statistical significance of estimated parameters. In the main part of the paper we will work with the sample of almost three hundred U.S. commercial banks which will be separate into two groups (non-default and default) on the basis of historical information. Subsequently, we will stepwise apply the mentioned above scoring models on this sample to derive several models for estimation of PD. Further we will apply these models to the control sample to determine the most appropriate model.
dcterms:title
Comparison of Credit Scoring Models on Probability of Default Estimation for US Banks Comparison of Credit Scoring Models on Probability of Default Estimation for US Banks
skos:prefLabel
Comparison of Credit Scoring Models on Probability of Default Estimation for US Banks Comparison of Credit Scoring Models on Probability of Default Estimation for US Banks
skos:notation
RIV/61989100:27510/13:86082949!RIV14-MSM-27510___
n7:predkladatel
n12:orjk%3A27510
n3:aktivita
n17:S n17:P
n3:aktivity
P(EE2.3.20.0296), S
n3:cisloPeriodika
2
n3:dodaniDat
n6:2014
n3:domaciTvurceVysledku
n11:7110472 n11:6646980
n3:druhVysledku
n16:J
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
66168
n3:idVysledku
RIV/61989100:27510/13:86082949
n3:jazykVysledku
n14:eng
n3:klicovaSlova
Probit Regression.; Logistic Regression; Linear Discriminant Analysis; Credit Scoring Models; Probability of Default (PD)
n3:klicoveSlovo
n9:Credit%20Scoring%20Models n9:Linear%20Discriminant%20Analysis n9:Probability%20of%20Default%20%28PD%29 n9:Probit%20Regression. n9:Logistic%20Regression
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[7E0E0AEFAA17]
n3:nazevZdroje
Prague Economic Papers
n3:obor
n19:AH
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n18:EE2.3.20.0296
n3:rokUplatneniVysledku
n6:2013
n3:svazekPeriodika
22
n3:tvurceVysledku
Gurný, Petr Gurný, Martin
n3:wos
000321990300002
s:issn
1210-0455
s:numberOfPages
19
n10:organizacniJednotka
27510