| 期刊全稱 | Algorithmic Differentiation in Finance Explained | | 影響因子2023 | Marc Henrard | | 視頻video | http://file.papertrans.cn/153/152926/152926.mp4 | | 發(fā)行地址 | Discusses Algorithmic Differentiation specifically applied to finance.Provides guidance on theory and the practical application to financial markets.Offers working code for testing and analysis | | 學(xué)科分類 | Financial Engineering Explained | | 圖書封面 |  | | 影響因子 | .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 all the major applications of AD in the derivatives setting with a focus on implementation..Algorithmic Differentiation (AD) has been popular in engineering and computer science, in areas such as fluid dynamics and 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 and a large amount of computer power, which in prohibitively expensive and can be time consuming.? Algorithmic differentiation techniques can 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 all the major applications of AD in the derivatives se | | Pindex | Book 2017 |
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