Simulation Techniques in Financial Risk Management

By Prakash, Puneet | Journal of Risk and Insurance, June 2009 | Go to article overview

Simulation Techniques in Financial Risk Management


Prakash, Puneet, Journal of Risk and Insurance


Simulation Techniques in Financial Risk Management, by Ngai Hang Chan and Hoi Ying Wong, 2006, John Wiley & Sons, Hoboken, NJ, pp. 240. ISBN: 978-0-471-46987-2

Reviewer: Puneet Prakash, Virginia Commonwealth University

The Wiley InterScience "Statistics in Practice" series aims to provide both practitioners and research workers with statistical techniques for their respective disciplines, and this book is no exception. Although the authors' intended audience is practitioners in financial risk management, the book is also a useful tool for graduate students in the field because it provides concise simulation methodologies for many financial risk models.

Simulation is a necessity in financial risk management, allowing practitioners to solve many problems that lack closed-form solutions. The book is perfectly positioned between Ross (2002) and Glasserman (2004) and is a valuable intermediate-level text. It contains a semester's worth of topics in financial risk management, provided the course is taught only through simulations. The book does an excellent job of explaining why simulations are important in general as controlled experiments, as well as why, specifically, they are valued in the financial risk discipline.

Although the authors require basic exposure to probability and statistics at the undergraduate level, prior knowledge of mathematics/statistics at the graduate level would also be helpful. For example, a reader with only the recommended exposure to mathematics/statistics at the Hogg and Tanis (2006) level would have difficulty grasping the difference between Stratonovich and Ito integrals (Chapter 2, exercise 5).

The authors of the text are statisticians, and the book bears their mark. Examples 1.3.1 and 1.3.2 would have fit a math/statistics text (see Hubbard and Hubbard, 1999) very well. Notation and results are usually introduced first, while intuition associated with symbols and their definitions follow later. Because this is a text presumably aimed at first-time readers, a reverse approach might be more suitable.

In the preface, the authors correctly state that the book requires a rudimentary knowledge of finance. However, when the authors say that they aim to strike a balance between theory and applications of risk management, what they really mean by theory is statistical theory. Readers expecting a more rigorous treatment of financial theory will be disappointed. Even though the field of risk management is an amalgamation of disciplines, and the authors mention finance, statistics, mathematics, and computer science,1 a background on financial risk is missing. Hence, from a purists' viewpoint, the book lacks financial theory but provides excellent computational tools for someone trained in financial economics to pick up valuable skills of simulation-based problem solving. As a financial risk resource, most gaps arise because the book has been written primarily from a statistical perspective rather than a financial economics one.

The book is technically quite sound. However, for readers trained in mathematics or statistics, it whets the appetite but leaves them wanting for more. The strengths of the text lie in the details and explanations of intuitive subtleties behind the equations, which many mathematical/statistical texts fail to highlight. Simple things like why ?p- is only a notation and not a derivative, explanation of Ito's Lemma from a nonmath/stat student's perspective are nuances that are easy to overlook. However, the authors are conscientious enough to give them due attention, and even harder concepts are sometimes also made to appear very easy. The simulation of look back options in Chapter 7 exemplifies this.

Some concepts are illustrated in a manner that even advanced readers will appreciate. The derivation of the Black-Scholes-Merton model of option pricing from the binomial model is explained extremely well. Nuances like why the mean function alone can be misleading in describing the stock price process are explained, and the material on simulation of the Greeks and exotic options is treated excellently. …

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