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A Workout in Computational Finance

Michael Aichinger
Organization: uni software plus GmbH & RICAM
Andreas Binder
Organization: MathConsult GmbH
Book information

Publisher: John Wiley & Sons (West Sussex)
Copyright year: 2013
ISBN: 9781119971917
Medium: Hardcover
Includes: website
Pages: 320
Out of print?: N
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Book cover image

Quantitative skills are a prerequisite for anyone looking to work in the finance industry today. Within the industry, any risk professional who wants to collaborate with, or work in most front office departments needs a thorough grounding in numerical methods, and the ability to assess their quality, their advantages and their limitations.

A Workout in Computational Finance delivers a profound and hands-on account of numerical methods used in modern quantitative finance, covering valuation and risk analysis of financial instruments from vanilla bonds to complex structures. The presented algorithms include, amongst others, tree methods, finite differences and finite elements, efficient Monte Carlo methods and Fourier techniques. Local and global optimisation techniques as well as stabilising regularisation methods for model calibration are thoroughly analysed.

The authors originate from the fields of theoretical physics and industrial mathematics, respectively, and have spent their professional careers creating efficient software solutions for producing industries and for financial industries. This book develops algorithms from the ground up, thus giving the reader a sound overview of their relative strengths and weaknesses. It is aimed at practitioners in the financial industry, for whom this is key knowledge in order to achieve optimal results with available data. It also enables junior quants with an IT background to implement numerical algorithms that work right away.

A Workout in Computational Finance is accompanied by a range of worked-out examples available from

*Business and Economics
*Business and Economics > Finance
*Mathematica Technology > Application Packages > Applications from Independent Developers > UnRisk STANDARD