Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks

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Abstract

We show how Adjoint Algorithmic Differentiation can be combined with the so-called Pathwise Derivative and Likelihood Ratio Method to construct efficient Monte Carlo estimators of second order price sensitivities of derivative portfolios. We demonstrate with a numerical example how the proposed technique can be straightforwardly implemented to greatly reduce the computation time of second order risk.

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APA

Capriotti, L. (2015). Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks. Algorithmic Finance, 4(1–2), 81–87. https://doi.org/10.3233/AF-150045

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