Algorithmic Differentiation in Finance Explained (Financial Engineering Explained)

Amazon.com Price: $29.99 (as of 11/10/2019 15:35 PST- Details)

Description

This book provides the first practical guide to the function and implementation of algorithmic differentiation in finance. Written in a highly accessible way, Algorithmic Differentiation Explained will take readers through the entire major applications of AD within the derivatives setting with a focal point on implementation.

Algorithmic Differentiation (AD) has been popular in engineering and computer science, in areas such as fluid dynamics and data assimilation for a few years.  Over the past decade, it has been an increasing number of (and successfully) applied to financial risk management, where it provides an efficient way to obtain financial instrument price derivatives with respect to the data inputs. Calculating derivatives exposure across a portfolio is no simple task.  It requires many complex calculations and a considerable amount of computer power, which in prohibitively expensive and may also be time consuming.  Algorithmic differentiation techniques may also be very successfully in computing Greeks and sensitivities of a portfolio with machine precision.

Written by a leading practitioner who works and programmes AD, it offers a practical analysis of the entire major applications of AD within the derivatives setting and guides the reader towards implementation.  Open source code of the examples is supplied with the book, with which readers can experiment and perform their very own test scenarios without writing the related code themselves.

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