
This book provides the first practical guide to the function & implementation of algorithmic differentiation in finance Written in a highly accessible way Algorithmic Differentiation Explained will take readers through all the major applications of AD in the derivatives setting with a focus on implementation Algorithmic Differentiation (AD) has been popular in engineering & computer science in areas such as fluid dynamics & data assimilation for many years Over the last decade it has been increasingly (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 & a large amount of computer power which in prohibitively expensive & can be time consuming Algorithmic differentiation techniques can be very successfully in computing Greeks & sensitivities of a portfolio with machine precision Written by a leading practitioner who works & programmes AD it offers a practical analysis of all the major applications of AD in the derivatives setting & guides the reader towards implementation Open source code of the examples is provided with the book with which readers can experiment & perform their own test scenarios without writing the related code themselves