Algorithmic Hessians and the Fast Computation of Cross-Gamma Risk

Joshi, Mark S.; Yang, Chao

doi:10.2139/ssrn.1626547

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…In the last several years quite a few papers were added to the literature on adjoint/AD applied to computational finance [11,22,20,21,24,23,32,30,31,41,50,48,49,54,52,55,56,65,3,9,18,67] . For selected papers we give an overview in the following sections…”

Section: Example In Calibration/optimization Frameworkmentioning

confidence: 99%

“…Adjoint approach was applied for computation of higher order Greeks (such as Gammas) in [52] and [54], where they show that the Gamma matrix (i.e. the Hessian) can be computed in AM + B times the number of operations where M is the maximum number of state variables required to compute the function, and A,B are constants that only depend on the set of floating point operations allowed..…”

Section: Computation Of Greeksmentioning

confidence: 99%

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Adjoints and Automatic (Algorithmic) Differentiation in Computational Finance

Homescu¹

2011

SSRN Journal

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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. *

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