The devastating impacts of the recent global financial crisis underscore the need for both
financial institutions and banking supervision to develop more appropriate credit risk models
to ensure the stability of the financial system. This work contributes to quantitative credit
portfolio risk modeling in three ways. First it introduces a general credit portfolio modeling
concept that comprises specific credit risk management models as special cases. Second
analytical techniques are presented for specifying asset correlations in a credit portfolio
through systematic factors. Finally a new approach for clustering of obligors in a credit
portfolio is proposed using threshold accepting a stochastic optimization technique. In
particular a computationally tractable technique to validate ex-post the precision of the
clustering system is suggested and applied to a real world retail credit portfolio. The
contributions of this book should provide benefit to practitioners academics and graduate
students in the field of financial risk management.
