George Michailides

English

Research Papers in Economics

JSS Journal of Statistical Software
June 2004, Volume 11, Book Review 4. http://www.jstatsoft.org/
Reviewer: George Michailidis
University of Michigan
Statistical Analysis of Financial Data in S-PLUS
Ren´ e A. Carmona
Springer, New York, 2004.
ISBN 0-387-20286-2. xvi + 451 pp. $79.95.
This book is intended primarily for an undergraduate course in ﬁnancial engineering. It
provides a very nice introduction to exploratory and model building approaches for analyzing
and understanding ﬁnancial data. The main concepts and techniques presented in the book
are illustrated in the S-PLUS computing environment.
The book is divided into three parts. The ﬁrst part introduces the readers to univariate
and multivariate exploratory data analytic techniques, the second part covers parametric and
nonparametric regression models and the third part discusses linear and nonlinear time series
models. The statistical techniques are illustrated by familiar to ﬁnance students concepts,
such as yield curve estimation, value-at-risk computation, option pricing, term structure of
interest rates, the CAPM model, etc. This way the student gets a ﬁrm grasp of how data
can be used to estimate models covered in an introductory ﬁnance course. Every chapter is
complemented with several theoretical and empirical homework problems. Finally, there is a
40-page appendix that provides a quick introduction to S-PLUS.
The breadth of topics covered is impressive; from exploratory graphics, to classical linear re-
gression, to kernel and projection pursuit regression, to univariate ARMA models, to Kalman
ﬁltering to ARCH-GARCH and Hidden Markov models. However, the presentation of the
various concepts is at times fairly dense and at other times somewhat incomplete. Personally,
I like a succinct presentation style, but I suspect that an instructor may have to complement
the material in the book with lecture notes that provide additional details.
The book begins with a chapter on univariate exploratory analysis, with the emphasis being
on histograms and kernel density estimation and qq-plots. A nice feature of the book is the
emphasis placed on heavy tailed distributions which are explored through qq-plots and com-
plimentary cdf plots. The second chapter deals with multivariate data exploration; however,
this chapter is really a patchwork of various topics, ranging from the correlation coeﬃcient,
to copulas and simulating random samples to
Chapter 3 introduces the reader to parametric regression models. It discusses simple and
multiple linear regression, polynomial models and non-linear regression. A nice feature of
of this chapter is the discussion of L1 regression models. The next chapter covers local and2 Statistical Analysis of Financial Data in S-PLUS
nonparametric regression and discusses various scatterplot smoothers, kernel and projection
pursuit regression.
Chapter 5 discusses the classical time series models, such as autoregressive, moving average
and combinations of the two, while chapter 6 is a hodgepodge of various topics, such as
multivariate linear time series models, state space models, Kalman ﬁltering, etc. Finally, in
chapter 7 the very popular in ﬁnance ARCH and GARCH models are reviewed, together with
ﬁltering of nonlinear systems and discretization of continuous time ﬁnancial models.
Some particularly welcome overall features of this book are: (i) the reference notes at the
end of each chapter that provide a brief historical perspective and additional references for
readers interested in further exploring the topic, (ii) explanations of the various S-functions
used and in particular their idiosyncracies, and (iii) the illustration of the various models and
techniques with illuminating examples through interesting ﬁnancial data sets.
As can be seen from the chapters’ contents, the breadth in terms of topics covered of this
book is impressive. However, this comes at the cost of a more in depth coverage of many
topics. For example, inferential issues and model diagnostics are largely ignored. I would have
preferred a more selective choice of models and techniques and a more extensive discussion.
Moreover, despite the presence of many plots, even more and richer plots would have been
useful.
Another downside of this book is the use of the S-PLUS platform. Many departments have
switched from S-PLUS to R and I strongly suspect that they are not interested in dedicating
any ﬁnancial resources on acquiring an S-PLUS license. Therefore, a new edition of the book
that would substitute R for S-PLUS would be very welcome.
Overall, this is a very nice book for introducing students to a variety of models for analyzing
ﬁnancial data. However, the instructor should be prepared to provide extra material for many
of the topics discussed in the book.
Reviewer:
George Michailidis
University of Michigan
Department of Statistics
Ann Arbor, MI 48109, USA
E-mail: gmichail@umich.edu
Journal of Statistical Software Published: 2004-06-05
June 2004, Volume 11, Book Review 4.
http://www.jstatsoft.org/

Statistical Analysis of Financial Data in S-PLUS

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Research Papers in Economics