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Algorithmic Differentiation: Adjoint Greeks Made Easy

12 Pages Posted: 4 Apr 2011  

Luca Capriotti

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

Michael B. Giles

University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: April 2, 2011

Abstract

We show how algorithmic differentiation can be used as a design paradigm to implement the adjoint calculation of sensitivities in Monte Carlo in full generality and with minimal analytical effort. With several examples we illustrate the workings of this technique and demonstrate how it can be straightforwardly implemented to reduce the time required for the computation of the risk of any portfolio by orders of magnitude.

Keywords: Algorithmic Differentiation, Monte Carlo Simulations, Derivatives Pricing

Suggested Citation

Capriotti, Luca and Giles, Michael B., Algorithmic Differentiation: Adjoint Greeks Made Easy (April 2, 2011). Available at SSRN: https://ssrn.com/abstract=1801522 or http://dx.doi.org/10.2139/ssrn.1801522

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

Michael Giles

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

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