Rolling Adjoints: Fast Greeks along Monte Carlo scenarios for early-exercise options

In this paper we extend the stochastic grid bundling method (SGBM), a regress-later based Monte Carlo scheme for pricing early-exercise options, with an adjoint method to compute in a highly efficient manner sensitivities along the paths, with reasonable accuracy. With the ISDA standard initial margin model being adopted by the financial markets, computing sensitivities along scenarios is required to compute quantities like the margin valuation adjustment.

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article Rolling Adjoints: Fast Greeks along Monte Carlo scenarios for early-exercise options S. Jain (Shashi), Á. Leitao Rodriguez (Álvaro) and C.W. Oosterlee (Kees)