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This chapter is from the book

Direction of Quantitative Finance

It might seem that at least one branch of quantitative finance would become less complex as it enters the mainstream, but that is apparently not happening yet. How is it possible for the average investor to understand a security that requires a Ph.D. to design? Paul Wilmott, Michael Thomsett, and many others have advocated the practical use of quantitative methods, emphasizing more transparency in the use of derivatives. In 2008, Wilmott blogged:

  • In my view the main reason why quantitative finance is in a mess is because of complexity and obscurity. Quants are making their models increasingly complicated, in the belief that they are making improvements. This is not the case....finance is not a hard science, one in which you can conduct experiments for which the results are repeatable. Finance, thanks to it being underpinned by human beings and their wonderfully irrational behaviour, is forever changing. It is therefore much better to focus your attention on making the models robust and transparent rather than ever more intricate.7

He describes a “sweet spot” in quant finance. The sweet spot is where models are not too elementary to be of practical use, but not so abstract that even the inventors don’t really understand them. He adds, “I teach on the Certificate in Quantitative Finance, and in that, our goal is to make quant finance practical, understandable, and, above all, safe.”

I agree. That is why I have targeted a particular sweet spot in this book: the aspects of quantitative finance that are most helpful in designing and communicating structured securities.

This book introduces a new framework to illustrate the mechanics of option pricing. The logic behind option pricing serves as the basis for much of financial engineering, for building structured securities and evaluating alternative investment strategies. What makes the method different is that it uses a simplified spreadsheet to illustrate the “matrix” nature of the building blocks of quantitative finance: random variables. In random variable form, the underlying probabilities are kept transparent and are not condensed into formulas.

By keeping the probabilities separate, a number of calculations become much easier to understand, which, in turn, makes the securities evaluated on the same basis easier to understand.

I am excited about writing this book because of something that I stumbled across a few years ago that made the entire subject of quantitative finance easier for me. It involved a simple way to replicate complicated formulas. For me, the breakthrough came one night while I was practicing my putting stroke. I had an idea and decided to play with that instead.

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