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What Drives the Aggregate Credit Risk: The Case of the Czech Republic
Málek, Jan ; Seidler, Jakub (advisor) ; Doležel, Pavel (referee)
There has been a long discussion about macroeconomic variables influencing the level of aggregate credit risk in the economy. While literature provides both empirical evidence and theoretical explana- tion of the influence of the business cycle on credit risk, the effect of other macroeconomic variables has not been explored sufficiently. In addition, recent literature suggests the existence of a latent risk factor behind aggregate credit risk, which is regularly interpreted as the latent default cycle. This thesis provides in its first part a discussion of potential aggregate credit risk drivers, which have been previously suggested in literature. We verify using a linear regression model whether the effect of these macroeconomic variables is also apparent in the Czech Republic. Results seem to be stable for both different model specifications and different clients segments and are in line with previous studies. The second part of this thesis explicitly models the latent factor that is assumed behind aggregate credit risk by adding an unobserved component to the already existing model constructed earlier in this thesis. The unobserved component can be estimated by applying Kalman filter. We subsequently discuss the sources of the latent component and whether it can be interpreted as the default cycle. The...
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Joinpoint Regression
Lain, Michal ; Maciak, Matúš (advisor) ; Hlávka, Zdeněk (referee)
The theme of this thesis is the joinpoint regression, the description of model, its properties and its construction. We are interested in methods of estimating parameters. We show practical use of the model. In the first chapter we define the model, we describe alternative forms and properties. In the second chapter we focus on estimating parameters of model. We briefly mention of Hudson method, profile likelihood, grid search and LASSO. We mention likelihood ratio for testing hypotheses about values of parameters. The third chapter deals with comparison of models by number of break points by permutation tests and information cri- terions. In the fourth chapter we deal with practical examples. We show diverse application of the model. We compare methods using simulations and show model application. 1
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What Drives the Aggregate Credit Risk: The Case of the Czech Republic
Málek, Jan ; Seidler, Jakub (advisor) ; Doležel, Pavel (referee)
There has been a long discussion about macroeconomic variables influencing the level of aggregate credit risk in the economy. While literature provides both empirical evidence and theoretical explana- tion of the influence of the business cycle on credit risk, the effect of other macroeconomic variables has not been explored sufficiently. In addition, recent literature suggests the existence of a latent risk factor behind aggregate credit risk, which is regularly interpreted as the latent default cycle. This thesis provides in its first part a discussion of potential aggregate credit risk drivers, which have been previously suggested in literature. We verify using a linear regression model whether the effect of these macroeconomic variables is also apparent in the Czech Republic. Results seem to be stable for both different model specifications and different clients segments and are in line with previous studies. The second part of this thesis explicitly models the latent factor that is assumed behind aggregate credit risk by adding an unobserved component to the already existing model constructed earlier in this thesis. The unobserved component can be estimated by applying Kalman filter. We subsequently discuss the sources of the latent component and whether it can be interpreted as the default cycle. The...
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