We apply adjoint algorithmic differentiation (AAD) to the risk management of derivative securities in the situation where the dynamics of securities prices are given in terms of partial differential equations (PDE). With simple examples, we show how AAD can be applied to both forward and...

We present an arbitrage-free valuation framework for the counterparty exposure of credit derivatives portfolios based on a Clayton dynamical default dependency approach. The method is able to capture consistently the effects of credit spread volatility and credit correlations. By introducing...

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

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

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...