Algorithmic Differentiation in Finance: Root Finding and Least Square Calibration
OpenGamma Quantitative Research, n.7
20 Pages Posted: 9 Jan 2013 Last revised: 25 Mar 2013
Date Written: January 9, 2013
Algorithmic Differentiation (AD) is an efficient way to compute derivatives of a value with respect to the data inputs. In finance the model calibration to market data can be an important part of the valuation process. In presence of calibration, when obtained through exact equation solving or optimisation, very efficient implementation can be done using the implicit function theorem with the standard AD approach. Previous results discussed the exact case are here extended to the case of calibration obtained by a least-square approach.
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