An efficient algorithm to compute correlation Greeks

Antoine Vandendorpe

Journal of Computational Finance

Abstract: We develop a new algorithm that allows us to compute pairwise-correlation sensitivities in a Monte Carlo framework by modifying only one trajectory at a time, resulting in a significant decrease in Brownian noise, computing time and memory requirements. We apply this algorithm to the case of the risk management of a large portfolio of options on baskets of equities, but the same algorithm can be used for computing correlation sensitivities in any Monte Carlo framework. We show how this idea can also simplify the computation of correlation Greeks in the framework of adjoint algorithmic differentiation.

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