BibTeX
@ARTICLE{
         Capriotti2024yoA,
       author = "Luca Capriotti and Mike Giles",
       title = "15 years of {A}djoint {A}lgorithmic {D}ifferentiation ({AAD}) in finance",
       journal = "Quantitative Finance",
       volume = "24",
       number = "9",
       pages = "1353--1379",
       year = "2024",
       publisher = "Routledge",
       doi = "10.1080/14697688.2024.2325158",
       abstract = "Following the seminal ``Smoking Adjoint'' paper by Giles and Glasserman
         [Smoking adjoints: Fast monte carlo greeks. Risk, 2006, 19, 88--92], the development of Adjoint
         Algorithmic Differentiation (AAD) has revolutionized the way risk is computed in the financial
         industry. In this paper, we provide a tutorial of this technique, illustrate how it is immediately
         applicable for Monte Carlo and Partial Differential Equations applications, the two main numerical
         techniques used for option pricing, and review the most significant literature in quantitative
         finance of the past fifteen years.",
       ad_area = "Finance",
       ad_theotech = "General"
}