I am not a “quant” by trade; in fact, I have very little background in economics and finance. So why am I reviewing a book on quantitative finance? Well, I have always had a real fascination with finance and the analysis of financial markets, so when the opportunity to review a book on the “how to” of quantitative finance came up I jumped at the opportunity. One of the benefits of this book is that it only requires some familiarity with finance and no familiarity with the programming language R. The purpose of the book is to demonstrate how R can be used to help solve problems that are common in quantitative finance, such as asset pricing, derivatives, and portfolio optimization. Although no understanding of R is necessary and only a familiarity with finance is required, readers should have a grasp of statistics and, in the case of derivatives pricing, stochastic calculus.
The book is split into nine chapters, with the first chapter covering the basics of time series analysis and the remaining eight chapters covering various topics in quantitative finance. Each chapter is written by a different author, something that initially made me wary as often this can make the overall flow of a book quite stilted. Thankfully, this isn’t the case for this book. Although it is obvious that each chapter is written by a different author (they do all have their own writing styles after all), the book is not encumbered by the redundant repetition of basic concepts at the start of each chapter. The chapters are well structured and get to the point quickly, making it easy for the reader to get to the heart of the problem without being weighed down by unnecessary information. The way that the book is structured means that it is easy for each chapter to be used in isolation of the others. Assuming that the reader has no familiarity with R, it is advisable to read and work through chapter 1. After that, each chapter works as a separate entity; for example, the reader does not have to work through chapters 2 to 5 to be able to understand chapter 6.
One of the real strengths of this book is that all of the source code and data used is available for download from the publisher’s website (https://www.packtpub.com/big-data-and-business-intelligence/introduction-r-quantitative-finance). This means that the reader is able to follow all the code examples given in each chapter. In fact, this is something that I made sure to do, and as far as I can tell all of the given code examples and datasets work as expected.
As is inevitable with a book of this nature, some of the chapters are stronger than others. An example of this would be chapter 6: “Derivatives Pricing.” Although there is nothing fundamentally wrong with this chapter, its scope is limited. The chapter only focuses on option pricing and left me feeling that I wanted more by the time I reached the end. Furthermore, this book could benefit from a quick reference for R as an appendix. The authors state that no knowledge of R is needed, but a quick reference would be useful just to help the reader look up basic language concepts. I did find myself going to the Internet a few times to double check what the meaning of a statement was; a reference at the back of the book would have helped to overcome this.
Overall, this is a useful book for those wanting to understand how R can be used within the quantitative finance domain. As previously mentioned, it does need to be used with a reference for R, especially if the reader is not familiar with the language. However, the fact that each chapter stands on its own makes it a potentially valuable reference and starting point for those wanting to quickly grasp the fundamentals.
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