We show how algorithmic differentiation can be used as a design paradigm to implement the adjoint calculation of sensitivities in Monte Carlo in full generality and with minimal analytical effort. With several examples we illustrate the workings of this technique and demonstrate how it can be...

Adjoint algorithmic differentiation can be used to implement efficiently the calculation of counterparty credit risk. We demonstrate how this powerful technique can be used to reduce the computational cost by hundreds of times, thus opening the way to real time risk management in Monte Carlo

Adjoint Algorithmic Differentiation is one of the principal innovations in risk management of the recent times. In this paper we show how this technique can be used to compute real time risk for credit products