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Modelling Financial Derivatives with MATHEMATICA ®
 
 
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Modelling Financial Derivatives with MATHEMATICA ® (Hardcover)

by William T. Shaw (Author) "When expressed in mathematical terms, the modelling of a derivative security amounts to understanding the behaviour of a function of several variables in considerable detail..." (more)
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Product details

  • Hardcover: 550 pages
  • Publisher: Cambridge University Press (10 Dec 1998)
  • Language English
  • ISBN-10: 052159233X
  • ISBN-13: 978-0521592338
  • Product Dimensions: 25.4 x 19.5 x 3.1 cm
  • Average Customer Review: 4.0 out of 5 stars See all reviews (1 customer review)
  • Amazon.co.uk Sales Rank: 981,825 in Books (See Bestsellers in Books)

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Product Description

Review
‘Shaw’s book has the originality to present the standard mathematics of financial derivatives within a SAC [symbolic and algebraic computation]-assisted framework ... So, should we all become SAC experts? For many, the answer will probably be yes … There are many useful examples that it will often be a case of looking for the instrument most closely related to the desired one, and adapting the example code as required … Shaw’s book is definitely worth looking at for a few days to get a feel for the ideas and advantages that lie away from the traditional programming approaches - and then could be a strong basis for a longer-term view of the subject.’ Gabriele Susinno and Chris Shortland, RISK Magazine

Product Description
One of the most important tasks in finance is to find good mathematical models for financial products, in particular derivatives. However, the more realistic the model, the more practitioners face still-unsolved problems in rigorous mathematics and econometrics in addition to serious numerical difficulties. The idea behind this work is to use Mathematica to provide a wide range of exact benchmark models against which inexact models can be tested and verified. In so doing, the author is able to explain when models and numerical schemes can be relied on, and when they can’t. Benchmarking is also applied to Monte Carlo simulations. Mathematica’s graphical and animation capabilities are exploited to show how a model’s characteristics can be visualised in two and three dimensions, thereby gaining further insight. The models described are all available on an accompanying CD that runs on most Windows and Macintosh platforms; to be able fully to use the software, Mathematica 3.0 is required, though certain features are usable with Mathematica 2.2. This product will prove of inestimable worth for financial instrument valuation and hedging, checking existing models and for analysing derivatives; it can be used for professional or training purposes in financial institutions or universities, and on MBA courses.

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When expressed in mathematical terms, the modelling of a derivative security amounts to understanding the behaviour of a function of several variables in considerable detail. Read the first page
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4 of 4 people found the following review helpful:
4.0 out of 5 stars A comprehensive overview of established derivatives models, 27 Jul 1999
By A Customer
This book is commendable to those looking for a quick practitioner's introduction to established numerical techniques in derivatives modelling. It is compehensive enough to get you ready to code your own models immediately, using your own choice of finite difference or Monte Carlo schemes.

This is not to say that there are no analytic solutions presented in this book. Quite the opposite: I found the fact that a good third of the space is taken up by investigations into analytic models somewhat disappointing, as that is perhaps the area where Mathematica gives you the least advantage over other platforms.

The part dealing with finite difference and Monte Carlo schemes is excellent, however. The mathematics of the models is introduced in a very clear and concise fashion, and after this no-nonsense introduction you get straight into coding things up in Mathematica.

Against the background of the high-quality discussion of the issues that do find their way into this book, the number of currently important topics that are lacking treatment is regrettable. I would have particularly liked to see examples of inverse problems, letting Mathematica do the work of calibrating model parameters to more market observables than e.g. just constant stock volatility. Wouldn't we all love to use Mathematica for the calibration, as well as the evaluation and benchmarking of such hotly discussed models like stochastic volatility models or local volatility models? How much time we could save by not having to code all these steps in C++ or worse environments! It seems it would have been a small step for the author to take us that little bit further along, but a large step for the majority of the readership who doesn't share the author's proficiency in the use of Mathematica. Still, if this more advanced level of usage is your aim, the book will at least start you off on the right track.

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