15 Years of Adjoint Algorithmic Differentiation in Finance

29 Pages Posted: 30 Oct 2023

See all articles by Luca Capriotti

Luca Capriotti

Columbia University

Michael B. Giles

University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: September 30, 2023

Abstract

Following the seminal “Smoking Adjoint” paper by Giles and Glasserman, 2006, the development of Adjoint Algorithmic Differentiation (AAD) has revolutionized the way risk is computed in the financial industry. In this paper, we provide a tutorial of this technique, illustrate how it is immediately applicable for Monte Carlo and Partial Differential Equations applications, the two main numerical techniques used for option pricing, and review the most significant literature in quantitative finance of the past fifteen years.

Keywords: Algorithmic Differentiation, Monte Carlo Simulations, Partial Differential Equations, Derivatives Pricing, Calibration of Stochastic Models

Suggested Citation

Capriotti, Luca and Giles, Michael B., 15 Years of Adjoint Algorithmic Differentiation in Finance (September 30, 2023). Available at SSRN: https://ssrn.com/abstract=4588939 or http://dx.doi.org/10.2139/ssrn.4588939

Luca Capriotti (Contact Author)

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Michael B. 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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