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In a preface to
this book, author Ramaswamy explains that he originally planned it as a
non-technical book, but as writing progressed, he felt compelled to incorporate
more mathematics. The result is a practitioners' book that may be
too technical for non-technical readers but not technical enough for anyone
planning on implementing models.
Following a brief
introductory chapter, Chapter 2 reviews probability theory and matrix algebra.
This material will be appreciated by anyone who has not looked at these subjects
since their university days. Chapter 3 discusses—and largely promotes—corporate
bonds as an asset class. Chapter 4 briefly discusses market risk. The treatment
is somewhat idiosyncratic. While interesting, it is no substitute for a standard
book on market risk.
Chapters 5 through
10 are the meat of the book, focusing on credit risk modeling for portfolios of
corporate bonds. Discussions cover:
 single
obligor credit risk using credit ratings or structural models,
default
and loss correlations,
leptokurtic
distributions,
simulation
techniques,
risk
reporting, and
portfolio
optimization.
The book closes
with a chapter describing structural products—especially CDO's. This is somewhat
like an abbreviated version of Goodman and Fabozzi (2002).
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1. Introduction
2. Mathematical Preliminaries
3. The Corporate Bond Market
4. Modeling Market Risk
5. Modeling Credit Risk
6. Portfolio Credit Risk
7. Simulating the Loss Distribution
8. Relaxing the Normal Distribution Assumption
9. Risk Reporting and Performance Attribution
10. Portfolio Optimization
11. Structured Credit Products
Solutions to End-of-chapter Questions |
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A strength of this book is that it is written from
a portfolio manger's perspective. It illustrates how many theoretical concepts
might be useful in a practical context. It also offers interesting exercises
with solutions provided at the end of the book. One shortcoming is a parochial,
somewhat buy-side tone. The author tends to do things his own way. For example,
his use of delta is somewhat unique. Like many fixed income
portfolio managers, he exhibits an interest in principal component analysis that
exceeds that technique's practical usefulness. The primary shortcoming of the
book is a technical level that is probably not technical enough.
Important concepts such as option-adjusted spread analysis, stratified sampling
or linear programming are mentioned but not explained. Quantitative examples
tend to focus on easy aspects of problems and bypass what is challenging.
For non-technical readers, I think this book can
be a nice, slightly technical supplement for books such as Saunders and Allen (2002)
or Crouhy et al (2001).
For more technical readers, it might serve as a less technical supplement for
Bluhm et al (2001). The book
offers a good context for seeing how theory can be put to use.
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