2011/07/10 by Cristian Homescu, Homescu, Cristian
Economics, Econometrics and Finance · #Computational Finance (q-fin.CP) #FOS: Economics and business #Stochastic processes and financial applications #q-fin.CP
paper · pdf · doi:10.48550/arxiv.1107.1831
23 pages
arxiv created 2011/07/10 · openalex publication_date 2011/07/10 · arxiv updated 2011/07/12 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
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.