| Summary: | In competitive markets only the strong survive. For an institution to
survive it needs to achieve high levels of performance through continued
improvements and learning. Credit risk measurement has come under
intense scrutiny with the recent Basel II Accord, an Accord that obliges
banks to seek more efficient means for the management of their credit risk.
In this dissertation I examine Data Envelopment Analysis (DEA), its
extensions and applications to credit risk measurement. DEA is a
performance measurement technique used to evaluate relative efficiency of
a group. It is a quantitative model with a solid mathematical and
economic underpinning; solving several linear programs simultaneously. Its
greatest advantage is a feature that allows it to process multiple inputs
and multiple outputs, thus uncovering relationships which remain hidden
from other methodologies. We consider DEA as a useful tool for improving
credit risk measurement within peer group analysis, a supplement or
complement to the credit ratings and a validation tool for credit ratings.
We apply DEA models to two credit risk measurement areas: Corporate
Credit Risk and Country Risk. In the first application we apply DEA to
corporate entities and compare efficiency to corporate credit ratings.
Overall we find efficiency and credit rating have common elements. This
confirms the common sense notion that corporates receiving better credit
ratings are more efficient. In the second application we apply DEA to
countries. We identify those countries that are performing better than the
rest based on their efficiency. We discover that when a country’s efficiency
is compared to country credit rating, efficiency and credit rating are
measuring two different but equally important aspects. We find that
efficiency is a good indicator of relative economic acceleration. We observe
that efficiency has the potential to identify future improvements in country
credit quality. Based on the application of DEA to Corporate and Country
Credit Risk, we recommend the use of DEA as a complementary tool to
credit rating models and a means of facilitating better measurement and
management of credit risk within banks.
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