Abstract
Adjoint Algorithmic Differentiation (AAD) is one of the principal innovations in risk management of the recent times. In this talk I will show how AAD can be used to risk manage in real time credit products, both exotic and vanilla, whether valued with Monte Carlo or with semi-analytical methods. I will show how by combining adjoint ideas with the implicit function (Dini’s) theorem one can avoid repeating multiple times the calibration of the hazard rate curves which, especially for flow products, often represent the bottle neck in the computation of spread and interest rate risk. This typically results in orders of magnitudes savings in computation time with respect to standard methods. The adjoint of the calibration step can be naturally combined with the adjoint of the pricing step. This allows one to compute the risk of portfolios of credit products faster than computing the portfolio value alone, thus making risk management in real time possible.