Adjoints And Automatic (Algorithmic) Differentiation In Computational Finance

Two of the most important areas in computational finance: Greeks and, respectively, calibration, are based on efficient and accurate computation of a large number of sensitivities. This paper gives an overview of adjoint and Automatic Differentiation (AD), also known as algorithmic differentiation, techniques to calculate these sensitivities. When compared to finite difference approximation, this approach can potentially reduce the computational cost by several orders of magnitude, with sensitivities accurate up to machine precision. Examples and a literature survey are also provided.

Provided by: Cornell University Topic: CXO Date Added: Jul 2011 Format: PDF

Find By Topic