Browsing by Subject "Logistic regression"

It is important that the financial system retains its functionality throughout the macroeconomic cycle. When people lose their trust in banks the whole economy can face dire consequences. Therefore accurate and stable predictions of the expected losses of borrowers or loan facilities are vital for the preservation of a functioning economy. The research question of the thesis is: What effect does the choice of calibration type have on the accuracy of the probability of default values predictions. The research question is attempted to be answered through an elaborate simulation of the whole probability of default model estimation exercise, with a focus on the rank order model calibration to the macroeconomic cycle. Various calibration functions are included in the study to offer more diversity in the results. Furthermore, the thesis provides insight into the regulatory environment of financial institutions, presenting relevant articles from accords, regulations and guidelines by international and European supervisory agents. In addition, the thesis introduces statistical methods for model calibration to the long-run average default rate. Finally, the thesis studies the effect of calibration type on the probability of default parameter estimation. The investigation itself is done by first simulating the data and then by applying multiple different calibration functions, including two logit functions and two Bayesian models to the simulated data. The simulation exercise is repeated 1 000 times for statistically robust results. The predictive power was measured using mean squared error and mean absolute error. The main finding of the investigation was that the simple grades perform unexpectedly well in contrast to raw predictions. However, the quasi moment matching approach for the logit function generally resulted in higher predictive power for the raw predictions in terms of the error measures, besides against the captured probability of default. Overall, simple grades and raw predictions yielded similar levels of predictive power, while the master scale approach lead to lower numbers. It is reasonable to conclude that the best selection of approaches according to the investigation would be the quasi moment matching approach for the logit function either with simple grades or raw predictions calibration type, as the difference in the predictive power between these types was minuscule. The calibration approaches investigated were significantly simplified from actual methods used in the industry, for example, calibration exercises mainly focus on the derivation of the correct long-run average default rate over time and this study used only the central tendency of the portfolio as the value.