ABSTRACT
Inspired by Giles and Glasserman [1], Algorithmic Differentiation (AD) [2,3] has been gaining popularity in computational finance over recent years. Adjoint AD (AAD) in particular facilitates a paradigm shift in financial modelling through provision of first-order sensitivities at a relative computational cost which is independent of the number of sensitivities asked for.
