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.
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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.).
- 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.
- C. Kaebe & J. Maruhn & E. Sachs, 2009. "Adjoint-based Monte Carlo calibration of financial market models," Finance and Stochastics, Springer, vol. 13(3), pages 351-379, September.
- Gabriel Turinici, 2009. "Calibration of local volatility using the local and implied instantaneous variance," Post-Print hal-00338114, HAL.
- Joshi, Mark & Pitt, David, 2010. "Fast Sensitivity Computations for Monte Carlo Valuation of Pension Funds," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 40(02), pages 655-667, November.
- Joshi, Mark & Yang, Chao, 2011. "Efficient greek estimation in generic swap-rate market models," Algorithmic Finance, IOS Press, vol. 1(1), pages 17-33.
- Luca Capriotti & Mike Giles, 2010. "Fast Correlation Greeks by Adjoint Algorithmic Differentiation," Papers 1004.1855, arXiv.org.
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