AAD = Adjoint Algorithmic Differentiation

AAD is a mathematical technique used to significantly speed up the calculation of sensitivities of derivatives prices to underlying factors, called Greeks. It is widely used in the risk management of complex derivatives and valuation adjustments.

The technique has been shown to compute Greeks up to 1,000 times faster compared with the bumping method. Disadvantages of AAD include lengthy development time and the need for highly skilled quantitative programmers.