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There are many good technical books written on derivatives-related topics. With the exception of a few, many of them assume a certain level of mathematical familiarity and maturity with stochastic calculus – thereby making the materials almost nonaccessible or in comprehensible to practitioners (or wannabe practitioners) coming fresh out from an MBA or an undergraduate program, or who are eager to roll up their sleeves to learn such tools themselves. On the other hand, the books that provide a good foundation on derivatives (with very little emphasis on stochastic calculus), do not go far enough to provide the mathematical tools that are often necessary to solve the practical problems encountered by a risk manager, analyst, or an operations manager.

The quantitative tools required by a risk practitioner are unique compared to the tools required by practitioners in other disciplines; these tools heavily borrow from and combine fields of pure mathematics, applied mathematics, probability, statistics, computer science, and so on – very much akin to the operations research field. As a consequence, any beginner without a good grasp of the tools can easily get lost in this quantitative maze. Based on my extensive risk-modelling experience across different businesses, I have found that while it is good for any aspiring professional to understand the mathematical rigor motivating these tools,[1] it is more important for someone to be able to understand the tools that are available, the strengths and weaknesses (limitations) of such tools, know how to apply the tools effectively, and fully comprehend what the risks are – all the while being able to ask the right questions without losing sight of common sense. As a consequence, given the power of personal computers and the ready availability of good quantitative commercial software packages like Microsoft Excel, @RISK, Matlab, and so on, it suffices for practitioners to understand the heuristics associated with the applications of these tools and how these tools can be adapted and customized for the needs of solving the particular problem.

Since this book is targeted to practitioners (and wannabe practitioners), I have kept the contents of this book to the discussion of practical issues and how quantitative tools are used to solve these problems, while leaving the review of such quantitative tools to the website accompanying this book. I have also had the luxury of using parts of this book to teach undergraduate and graduate students in business and financial engineering programs, as well as professionals on quantitative and modeling techniques. Given the number of students that I have taught over the decades with versions of this material, I think it is fairly safe to say that for readers to extract maximum value from this book they should have some level of familiarity with basic derivatives and financial concepts, undergraduate calculus, probability, and statistics. Although this book should be useful to any practitioner on a standalone basis, it can easily complement many of the widely used textbooks on derivatives, finance, and operations research.

  • [1] Examples of this would be the study of measure theory and diffusion processes using stochastic calculus.
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