Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks

Luca Capriotti ()
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Luca Capriotti: Quantitative Strategies, Investment Banking Division, Credit Suisse Group, Postal: One Cabot Square, London E14 4QJ, United Kingdom

Algorithmic Finance, 2015, vol. 4, issue 1-2, 81-87

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.

Keywords: Adjoint Algorithmic Differentiation; Monte Carlo; derivatives securities; risk management (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0038

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