Autor/Autorin: Málek Jan
Jan Málek is a lifelong unthusiast with his research focus covering the area of risk management in both the banking and the energy sector.
There has been a long discussion about macroeconomic variables influencing the level of aggregate credit risk in the economy. While literature provides both evidence and theoretical explanation of the influence of the business cycle on credit risk, the effect of other variables has not been explored sufficiently. In addition, recent literature suggests the existence of a latent factor behind aggregate credit risk. This work provides in its first part a discussion of potential aggregate credit risk drivers, which have been previously suggested in literature. We verify using a regression model whether the effect of these variables is also apparent in the Czech Republic. The second part of this work explicitly models the latent factor by adding an unobserved component to the model constructed earlier in this thesis. The contribution of this work is due to our belief twofold. First, we add a latent component to the linear regression model. Secondly, we analyze if and under which circumstances the latent component extension improves the fit of the regression model and discuss whether the explicit estimate of the unobserved component has a feasible interpretation as the default cycle.
Über den Autor
Jan Málek is a lifelong unthusiast with his research focus covering the area of risk management in both the banking and the energy sector.
Klappentext
There has been a long discussion about macroeconomic variables influencing the level of aggregate credit risk in the economy. While literature provides both evidence and theoretical explanation of the influence of the business cycle on credit risk, the effect of other variables has not been explored sufficiently. In addition, recent literature suggests the existence of a latent factor behind aggregate credit risk. This work provides in its first part a discussion of potential aggregate credit risk drivers, which have been previously suggested in literature. We verify using a regression model whether the effect of these variables is also apparent in the Czech Republic. The second part of this work explicitly models the latent factor by adding an unobserved component to the model constructed earlier in this thesis. The contribution of this work is due to our belief twofold. First, we add a latent component to the linear regression model. Secondly, we analyze if and under which circumstances the latent component extension improves the fit of the regression model and discuss whether the explicit estimate of the unobserved component has a feasible interpretation as the default cycle.
