Adjoint Algorithmic Differentiation: Calibration and Implicit Function Theorem

OpenGamma Quantitative Research No. 1

14 Pages Posted: 3 Sep 2011 Last revised: 14 Apr 2013

See all articles by Marc P. A. Henrard

Marc P. A. Henrard

muRisQ Advisory; OpenGamma; University College London - Department of Mathematics

Date Written: September 1, 2011

Abstract

Adjoint Algorithmic Differentiation is an efficient way to obtain financial instrument price derivatives with respect to the data inputs. Often the differentiation does not cover the full pricing process when a model calibration is performed. Thanks to the implicit function theorem, the differentiation of the solver embedded in the calibration is not required to differentiate to full pricing process. An efficient approach to the full process differentiation is described.

Keywords: Financial model calibration, adjoint algorithmic differentiation, implicit function theorem, equation solver, efficient derivatives computation

Suggested Citation

Henrard, Marc P. A., Adjoint Algorithmic Differentiation: Calibration and Implicit Function Theorem (September 1, 2011). OpenGamma Quantitative Research No. 1. Available at SSRN: https://ssrn.com/abstract=1896329 or http://dx.doi.org/10.2139/ssrn.1896329

Marc P. A. Henrard (Contact Author)

muRisQ Advisory ( email )

Rue du Chemin de fer, 8
Brussels, 1210
Belgium

HOME PAGE: http://murisq.com

OpenGamma ( email )

Albert House
256-260 Old Street
London, EC1V 9DD
United Kingdom

University College London - Department of Mathematics ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Register to save articles to
your library

Register

Paper statistics

Downloads
683
rank
36,232
Abstract Views
2,582
PlumX Metrics
!

Under construction: SSRN citations will be offline until July when we will launch a brand new and improved citations service, check here for more details.

For more information