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Models of default prediction of a client
Hezoučká, Šárka ; Černý, Rostislav (advisor) ; Hurt, Jan (referee)
The aim of the presented work is to investigate possible improvement of scor- ing models prediction power in retail credit segment by using structural models estimating the future development of behavioral score. These models contain the information about past development of the behavioral score by parameters which take into account the sensitivity of clients' probability of default on in- dividual market and life changes. These parameters are estimated with Markov Chain Monte Carlo methods based on score history. Eight different types of struc- tural models were applied on the real data. The diversification measure of indivi- dual models is compared using the Gini coefficient. These structural models were compared with each other and also with the existing scoring model of the credit institution which provided the underlying data. 1
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MCMC methods for financial time series
Tritová, Hana ; Pawlas, Zbyněk (advisor) ; Komárek, Arnošt (referee)
This thesis focuses on estimating parameters of appropriate model for daily returns using the Markov Chain Monte Carlo method (MCMC) and Bayesian statistics. We describe MCMC methods, such as Gibbs sampling and Metropolis- Hastings algorithm and their basic properties. After that, we introduce different financial models. Particularly we focus on the lognormal autoregressive model. Later we theoretically apply Gibbs sampling to lognormal autoregressive model using principles of Bayesian statistics. Afterwards, we analyze procedu- res, that we used in simulations of posterior distribution using Gibbs sampling. Finally, we present processed output of both simulated and real data analysis.
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Models of default prediction of a client
Hezoučká, Šárka ; Černý, Rostislav (advisor) ; Hurt, Jan (referee)
The aim of this thesis is to investigate possible improvement of scoring models prediction power in retail credit segment by using structural models estimating the future development of behavioral score. These models contain the informa- tion about past development of the behavioral score by parameters which take into account the sensitivity of clients' probability of default on individual market and life changes. These parameters are estimated by Markov Chain Monte Carlo methods based on score history. Eight different types of structural models were applied to real data. The diversification measure of individual models is compared using the Gini coefficient. These structural models were compared with each other and also with the existing scoring model of the credit institution which provided the underlying data. 1
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Models of default prediction of a client
Hezoučká, Šárka ; Černý, Rostislav (advisor) ; Hurt, Jan (referee)
The aim of the presented work is to investigate possible improvement of scor- ing models prediction power in retail credit segment by using structural models estimating the future development of behavioral score. These models contain the information about past development of the behavioral score by parameters which take into account the sensitivity of clients' probability of default on in- dividual market and life changes. These parameters are estimated with Markov Chain Monte Carlo methods based on score history. Eight different types of struc- tural models were applied on the real data. The diversification measure of indivi- dual models is compared using the Gini coefficient. These structural models were compared with each other and also with the existing scoring model of the credit institution which provided the underlying data. 1
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