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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Building credit scoring models using selected statistical methods in R
 Jánoš, Andrej ; Bašta, Milan (advisor) ; Pecáková, Iva (referee)
Credit scoring is important and rapidly developing discipline. The aim of this thesis is to describe basic methods used for building and interpretation of the credit scoring models with an example of application of these methods for designing such models using statistical software R. This thesis is organized into five chapters. In chapter one, the term of credit scoring is explained with main examples of its application and motivation for studying this topic. In the next chapters, three in financial practice most often used methods for building credit scoring models are introduced. In chapter two, the most developed one, logistic regression is discussed. The main emphasis is put on the logistic regression model, which is characterized from a mathematical point of view and also various ways to assess the quality of the model are presented. The other two methods presented in this thesis are decision trees and Random forests, these methods are covered by chapters three and four. An important part of this thesis is a detailed application of the described models to a specific data set Default using the R program. The final fifth chapter is a practical demonstration of building credit scoring models, their diagnostics and subsequent evaluation of their applicability in practice using R. The appendices include used R code and also functions developed for testing of the final model and code used through the thesis. The key aspect of the work is to provide enough theoretical knowledge and practical skills for a reader to fully understand the mentioned models and to be able to apply them in practice.
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GARCH models and R
 Jánoš, Andrej ; Bašta, Milan (advisor) ; Hejdová, Martina (referee)
The work is devoted to the concept of volatility and the basic models of volatility ARCH and GARCH. Firstly, volatility, properties of volatility, general structure of the models and historical volatility is described. Then the ARCH and GARCH volatility models are introduced and their properties, estimation methods and the possibility of implementation of these models in modeling and forecasting volatility are discussed. A substantial part of this work is a detailed application of the described models to some particular time series (both simulated and real) using the R program. We analyze the real data capturing the evolution of Prague stock index PX. The key aspect of the work is to provide enough theoretical knowledge and practical skills for a reader to fully understand the mentioned models and to be able to apply them in practise.
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