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.
CITATION STYLE
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
Mendeley helps you to discover research relevant for your work.