Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks

Algorithmic Finance (2015), 4:1-2, 81-87

8 Pages Posted: 13 Oct 2014 Last revised: 28 Jul 2015

Luca Capriotti

Quantitative Strategies - Investment Banking Division - Credit Suisse Group; University College London

Date Written: May 1, 2015

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, Likelihood Ratio Method, Derivatives Pricing, Pathwise Derivative Method, Monte Carlo

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

Luca Capriotti (Contact Author)

Quantitative Strategies - Investment Banking Division - Credit Suisse Group ( email )

Eleven Madison Avenue
New York, NY 10010
United States

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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