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

We develop a novel stochastic valuation and premium calculation principle based on probability measure distortions that are induced by quantile processes in continuous time. Necessary and sufficient conditions are derived under which the quantile processes satisfy first– and second– order...