Adjoint Algorithmic Differentiation: Real-Time Counterparty Credit Risk Management in Monte Carlo Simulations

Luca Capriotti, Jacky Lee


One the most active areas of risk management today is counterparty credit risk management (CCRM). Managing counterparty risk is particularly challenging because it requires the simultaneous evaluation of all the trades facing a given counterparty. For multi-asset portfolios this typically comes with extraordinary computational challenges.

Indeed, with the exclusion of the simplest portfolios of vanilla instruments, computationally intensive Monte Carlo simulations are often the only practical tool available for this task. Standard approaches for the calculation of risk require repeating the calculation of the profit and loss (P&L) of the portfolio under hundreds of market scenarios. As a result, in many cases these calculations cannot be completed in a practical amount of time, even employing a vast amount of computer power. Since the total cost of the through-the-life risk management can determine whether it is profitable to execute a new trade, solving this technology problem is critical in order to allow a securities firm to remain competitive.

Following the introduction of adjoint methods in finance (Giles and Glasserman 2006), a computational technique dubbed “adjoint a

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