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This book provides the first practical guide to financial algorithm differentiation capabilities and implementation. Writing in a highly accessible manner Differential interpretation of the algorithm will enable the reader to understand all the major applications of AD in derived practice and focus on implementation.
Algorithmic differential (AD) in engineering and computer science fields such as fluid dynamics and data assimilation for many years. Over the past decade, it has increasingly (and successfully) applied to financial risk management, providing an effective way for financial instrument price derivatives to obtain data input. It is not easy to calculate the derivatives exposure in the portfolio. It requires many complex calculations and a lot of computer power, and these computing powers are too expensive and can be very time consuming. Algorithmic differentiation techniques can be very successful in calculating the sensitivity of Greek and machine-precision portfolios.
written by a leading practitioner of work and planning AD, it provides a practical analysis of AD settings in all major applications and guides readers to implement. This book provides the open source of these examples, which allows readers to experiment and execute their own test scenarios without having to write the relevant code themselves.
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Orignal From: Differentiation of Algorithms in Financial Analysis
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