Artificial Intelligence Techniques for Credit Risk Management
Pp. 268-293 (26)
Abdolreza Nazemi and Konstantin Heidenreich
For calculating the expected loss besides the exposure at default two
measures, namely the probability of default and the loss given default (LGD), have to
be taken into account. While in literature much attention has been paid to the default
rate the loss given default is still comparatively less investigated. Especially, as a
consequence of the enhanced regulation by Basel II accord loss given default has
become a much more critical measure for banks and other financial institutions as it has
Therefore, in this study artificial intelligence and statistical techniques are used to
predict the recovery rate of corporate bonds that defaulted between 2002 and 2012.
Macroeconomic factors, bond characteristics and industry specific factors are taken
into account as covariates for the techniques. Starting from the base case of a plainvanilla
Least Squares-Support Vector Machine (LS-SVM) two further modifications of
a LS-SVM are presented. The performance of the LS-SVM happens to be significantly
better than the performance of a casual linear regression approach. So, it is empirically
shown that support vector regression is an approach to LGD modeling which has
significant potential to be used for forecasts of the recovery rate both for banks and
other financial institutions as well as for investors in distressed debt.
Loss given default, Recovery rate, Credit Risk, Support Vector
School of Economics and Business Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany.