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Computational finance : an introductory course with R
Arratia A., Atlantis Publishing Corporation, New York, NY, 2014. 301 pp. Type: Book (978-9-462390-69-0)
Date Reviewed: Nov 17 2014

A person who is starting to learn quantitative investment, is acquainted with statistics, and has some experience with data processing tools like R, Statistica, or MATLAB will enjoy this book. It really provides what its title promises: it shows readers how to combine computational finance concepts with the analysis of data, supported by the well-respected free software language and environment R.

The book is just under 300 pages long. Arratia organizes the material in nine balanced chapters, each containing bibliographic remarks at the end, as well as some mathematics-based tasks and R-related exercises. At the end of the book, a concise appendix gives a short introduction to R. Although theoretically aimed at the lay reader, it is a good re-examination of, rather than introduction to, the software. In chapter 1, the author introduces basic notions related to finance (such as securities, interest rates, derivatives, cash flow models, and arbitrage). In the next chapters, Arratia deals with various statistical analysis methods related to finance: time series analysis in chapter 2; dependency analysis in chapter 3; ARMA, (G)ARCH, and semi-parametric modeling in chapter 4; price modeling in chapter 5; value estimation in chapter 6; the basics of optimization meta-heuristics in chapter 7; portfolio optimization in chapter 8; and finally decision making in real time in chapter 9. The material is presented with a broad range of applications in finance and with the use of R-related modeling.

A very nice aspect of this book is related to the auxiliary materials prepared by the author. Mainly, he has established and now maintains a webpage related to this work, where all of the problems presented can be downloaded. The reader is given instant access to the data and examples from the book. In this way, it can be treated as a good companion for people who would like to enter the world of finance and simultaneously practice R in this exciting new field.

To understand this book, it is not necessary to be trained in economics or related fields, but it is rather advisable to be acquainted with at least intermediate courses in calculus and statistics. As such topics are elements of typical master’s-level engineering curricula, this book will be enjoyed by all graduates, professional engineers, and computer scientists who would like to enter the field of finance with a tool like R that is almost surely known to them from other application areas. This volume adds to a rich library of books presenting the practical use of statistics with R [1,2].

Reviewer:  Piotr Cholda Review #: CR142938 (1502-0138)
1) Kleiber, C.; Zeileis, A. Applied econometrics with R. Springer Science+Business Media, New York, NY, 2008.
2) Beyersmann, J.; Allignol, A.; Schumacher, M. Competing risks and multistate models with R. Springer Science+Business Media, LLC, New York, NY, 2012.
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