From the late nineties, the spectacular growth of a secondary
market for credit through derivatives has been matched by the
emergence of mathematical modelling analysing the credit risk
embedded in these contracts. This book aims to provide a broad and
deep overview of this modelling, covering statistical analysis and
techniques, modelling of default of both single and multiple
entities, counterparty risk, Gaussian and non-Gaussian modelling,
and securitisation. Both reduced-form and firm-value models for the
default of single entities are considered in detail, with extensive
discussion of both their theoretical underpinnings and practical
usage in pricing and risk. For multiple entity modelling, the now
notorious Gaussian copula is discussed with analysis of its
shortcomings, as well as a wide range of alternative approaches
including multivariate extensions to both firm-value and reduced
form models, and continuous-time Markov chains. One important case
of multiple entities modelling - counterparty risk in credit
derivatives - is further explored in two dedicated chapters.
Alternative non-Gaussian approaches to modelling are also
discussed, including extreme-value theory and saddle-point
approximations to deal with tail risk. Finally, the recent growth
in securitisation is covered, including house price modelling and
pricing models for asset-backed CDOs. The current credit crisis has
brought modelling of the previously arcane credit markets into the
public arena. Lipton and Rennie with their excellent team of
contributors, provide a timely discussion of the mathematical
modelling that underpins both credit derivatives and
securitisation. Though technical in nature, the pros and cons of
various approaches attempt to provide a balanced view of the role
that mathematical modelling plays in the modern credit markets.
This book will appeal to students and researchers in statistics,
economics, and finance, as well as practitioners, credit traders,
and quantitative analysts.
General
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