Algorithmic Finance (2015), 4:1-2, 81-87
8 Pages Posted: 13 Oct 2014 Last revised: 28 Jul 2015
Date Written: May 1, 2015
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.
Keywords: Adjoint Algorithmic Differentiation, Likelihood Ratio Method, Derivatives Pricing, Pathwise Derivative Method, Monte Carlo
Suggested Citation: Suggested Citation
Capriotti, Luca, Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks (May 1, 2015). Algorithmic Finance (2015), 4:1-2, 81-87. Available at SSRN: https://ssrn.com/abstract=2508905 or http://dx.doi.org/10.2139/ssrn.2508905