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Discrimination measures in credit risk
Polak, Michal ; Pešta, Michal (advisor) ; Zahradník, Petr (referee)
Scoring models represent a fundamental tool for the modern management of credit risk. This is mainly due to a significant development in the field of information technology. Such models are used not only when providing credit, but also in strategies relating to the future management of credit risk, or in strategies connected with enforcing receivables. In my thesis I deal with discrimination measures used in the validation of diversification potential of logistic scoring models. At the beginning, I focus on the term 'risk'. Then, I introduce a basic division of scoring models. Next, I describe the method of scoring logistic regression, I concentrate on estimating parameters, their significance and on testing their relevance. For the measurement and illustration of diversification potential of the model I mention the most commonly used methods such as the Lorenz and ROC curve, the Gini coeficient, the c-statistic as well as the Kolmogorov-Smirnov test. Finally, I apply the theoretical knowledge to real data. I design a scoring model and subsequently compare the discrimination measures which it contains. Powered by TCPDF (www.tcpdf.org)
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Comparison of statistical methods for the scoring models development
Mrázková, Adéla ; Vitali, Sebastiano (advisor) ; Kopa, Miloš (referee)
The aim of this thesis is to introduce and summarize the process of scoring model development in general and then basic statistical approaches used to resolve this problem, which are in particular logistic regression, neural networks and decision trees (random forests). Application of described methods on a real dataset provided by PROFI CREDIT Czech, a.s. follows, including discussion of some implementation issues and their resolution. Obtained results are discussed and compared.
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Discrimination measures in credit risk
Polak, Michal ; Pešta, Michal (advisor) ; Zahradník, Petr (referee)
Scoring models represent a fundamental tool for the modern management of credit risk. This is mainly due to a significant development in the field of information technology. Such models are used not only when providing credit, but also in strategies relating to the future management of credit risk, or in strategies connected with enforcing receivables. In my thesis I deal with discrimination measures used in the validation of diversification potential of logistic scoring models. At the beginning, I focus on the term 'risk'. Then, I introduce a basic division of scoring models. Next, I describe the method of scoring logistic regression, I concentrate on estimating parameters, their significance and on testing their relevance. For the measurement and illustration of diversification potential of the model I mention the most commonly used methods such as the Lorenz and ROC curve, the Gini coeficient, the c-statistic as well as the Kolmogorov-Smirnov test. Finally, I apply the theoretical knowledge to real data. I design a scoring model and subsequently compare the discrimination measures which it contains. Powered by TCPDF (www.tcpdf.org)
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Discrimination measures in credit risk
Polak, Michal ; Pešta, Michal (advisor) ; Zahradník, Petr (referee)
Scoring models represent a fundamental tool for the modern management of credit risk. This is mainly due to a significant development in the field of information technology. Such models are used not only when providing credit, but also in strategies relating to the future management of credit risk, or in strategies connected with enforcing receivables. In my thesis I deal with discrimination measures used in the validation of diversification potential of logistic scoring models. At the beginning, I focus on the term 'risk'. Then, I introduce a basic division of scoring models. Next, I describe the method of scoring logistic regression, I concentrate on estimating parameters, their significance and on testing their relevance. For the measurement and illustration of diversification potential of the model I mention the most commonly used methods such as the Lorenz and ROC curve, the Gini coeficient, the c-statistic as well as the Kolmogorov-Smirnov test. Finally, I apply the theoretical knowledge to real data. I design a scoring model and subsequently compare the discrimination measures which it contains. Powered by TCPDF (www.tcpdf.org)
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Scoring Models in Finance (Skóringové modely ve financích)
Rychnovský, Michal ; Zouhar, Jan (advisor) ; Kalčevová, Jana (referee)
The aim of the present work is to describe the application of the logistic regression model to the field of probability of default modeling, and provide a brief introduction to the scoring development process used in financial practice. We start by introducing the theoretical background of the logistic regression model; followed by a consequent derivation of three most common scoring models. Then we present a formal definition of the Gini coefficient as a diversification power measure and derive the Somers-type formulas for its estimation. Finally, the key part of this work gives an overview of the whole scoring development process illustrated on the examples of real business data.
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