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Title Credit risk modeling
Author Einarsson, Arnar Ingi
Supervisor Madsen, Henrik (Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2008
Abstract The credit assessment made by corporate banks has been evolving in recent years. Credit assessments have evolved from the being the subjective assessment of the bank’s credit experts, to become more mathematically evolved. Banks are increasingly opening their eyes to the excessive need for comprehensive modeling of credit risk. The financial crisis of 2008 is certain to further the great need for good modeling procedures. In this thesis the modeling framework for credit assessment models is constructed. Different modeling procedures are tried, leading to the assumption that logistic regression is the most suitable framework for credit rating models. Analyzing the performance of different link functions for the logistic regression, lead to the assumption that the complementary log-log link is most suitable for modeling the default event. Validation of credit rating models lacks a single numeric measure that concludes the model performance. A solution to this problem is suggested by using principal component representatives of few discriminatory power indicators. With a single measure of model performance model development becomes a much more efficient process. The same goes for variable selection. The data used in the modeling process are not extensive as would be the case for many banks. An resampling process is introduced that is useful in getting stable estimates of model performance for a relatively small dataset.
Series IMM-M.Sc.-2008-100
Original PDF ep08_100.pdf (8.28 MB)
Admin Creation date: 2008-10-09    Update date: 2008-10-09    Source: dtu    ID: 224338    Original MXD