Numerical Methods in Finance with C++

Name: Numerical Methods in Finance with C++
SKU: 19774472
Price: 99.99 AUD
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Paperback / softback

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Mastering Mathematical Finance

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Description

Driven by concrete computational problems in quantitative finance, this book provides aspiring quant developers with the numerical techniques and programming skills they need. The authors start from scratch, so the reader does not need any previous experience of C++. Beginning with straightforward option pricing on binomial trees, the book gradually progresses towards more advanced topics, including nonlinear solvers, Monte Carlo techniques for path-dependent derivative securities, finite difference methods for partial differential equations, and American option pricing by solving a linear complementarity problem. Further material, including solutions to all exercises and C++ code, is available online. The book is ideal preparation for work as an entry-level quant programmer and it gives readers the confidence to progress to more advanced skill sets involving C++ design patterns as applied in finance.

Author Biography:

Maciej J. Capiński is an Associate Professor in the Faculty of Applied Mathematics at AGH University of Science and Technology in Krakow, Poland. His interests include mathematical finance, financial modelling, computer assisted proofs in dynamical systems and celestial mechanics. He has authored eight research publications and supervised over thirty MSc dissertations, mostly in mathematical finance. Tomasz Zastawniak holds the Chair of Mathematical Finance at the University of York. He has authored about fifty research publications and four books. He has supervised four PhD dissertations and around eighty MSc dissertations in mathematical finance.

Release date Australia

August 2nd, 2012

Author

Audiences

Professional & Vocational
Tertiary Education (US: College)

Illustrations

Worked examples or Exercises; 15 Line drawings, unspecified

Pages

175

Series

Mastering Mathematical Finance

Dimensions

152x227x11

ISBN-13

9780521177160

Product ID

19774472

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