The recent financial crisis has highlighted the need for better
valuation models and risk management procedures, better understanding of
structured products, and has called into question the actions of many
financial institutions. It has become commonplace to blame the
inadequacy of credit risk models, claiming that the crisis was due to
sophisticated and obscure products being traded, but practitioners have
for a long time been aware of the dangers and limitations of credit
models. It would seem that a lack of understanding of these models is
the root cause of their failures but until now little analysis had been
published on the subject and, when published, it had gained very limited
attention.
Credit Models and the Crisis is a succinct but technical analysis of
the key aspects of the credit derivatives modeling problems, tracing the
development (and flaws) of new quantitative methods for credit
derivatives and CDOs up to and through the credit crisis. Responding to
the immediate need for clarity in the market and academic research
environments, this book follows the development of credit derivatives
and CDOs at a technical level, analyzing the impact, strengths and
weaknesses of methods ranging from the introduction of the Gaussian
Copula model and the related implied correlations to the introduction of
arbitrage-free dynamic loss models capable of calibrating all the
tranches for all the maturities at the same time. It also illustrates
the implied copula, a method that can consistently account for CDOs with
different attachment and detachment points but not for different
maturities, and explains why the Gaussian Copula model is still used in
its base correlation formulation.
The book reports both alarming pre-crisis research and market examples,
as well as commentary through history, using data up to the end of 2009,
making it an important addition to modern derivatives literature. With
banks and regulators struggling to fully analyze at a technical level,
many of the flaws in modern financial models, it will be indispensable
for quantitative practitioners and academics who want to develop stable
and functional models in the future.
