Adjoints and Automatic (Algorithmic) Differentiation in Computational Finance
AbstractTwo 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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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1107.1831.
Date of creation: Jul 2011
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- Houtan Bastani & Luca Guerrieri, 2008. "On the application of automatic differentiation to the likelihood function for dynamic general equilibrium models," International Finance Discussion Papers 920, Board of Governors of the Federal Reserve System (U.S.).
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- Gabriel Turinici, 2009. "Calibration of local volatility using the local and implied instantaneous variance," Post-Print hal-00338114, HAL.
- Joshi, Mark & Yang, Chao, 2011. "Fast delta computations in the swap-rate market model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(5), pages 764-775, May.
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- Cristian Homescu, 2011. "Implied Volatility Surface: Construction Methodologies and Characteristics," Science & Finance (CFM) working paper archive 1107.1834, Science & Finance, Capital Fund Management.
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