Algorithmic Differentiation in Finance: Root Finding and Least Square Calibration

OpenGamma Quantitative Research, n.7

20 Pages Posted: 9 Jan 2013 Last revised: 25 Mar 2013

See all articles by Marc P. A. Henrard

Marc P. A. Henrard

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

Date Written: January 9, 2013

Abstract

Algorithmic Differentiation (AD) is an efficient way to compute derivatives of a value with respect to the data inputs. In finance the model calibration to market data can be an important part of the valuation process. In presence of calibration, when obtained through exact equation solving or optimisation, very efficient implementation can be done using the implicit function theorem with the standard AD approach. Previous results discussed the exact case are here extended to the case of calibration obtained by a least-square approach.

Suggested Citation

Henrard, Marc P. A., Algorithmic Differentiation in Finance: Root Finding and Least Square Calibration (January 9, 2013). OpenGamma Quantitative Research, n.7. Available at SSRN: https://ssrn.com/abstract=2198417 or http://dx.doi.org/10.2139/ssrn.2198417

Marc P. A. Henrard (Contact Author)

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OpenGamma ( email )

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