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# Remarks on stochastic automatic adjoint differentiation and financial models calibration

## Author

Listed:
• Dmitri Goloubentcev
• Evgeny Lakshtanov

## Abstract

In this work, we discuss the Automatic Adjoint Differentiation (AAD) for functions of the form $G=\frac{1}{2}\sum_1^m (Ey_i-C_i)^2$, which often appear in the calibration of financial models. This helps to understand the algorithm proposed recently in \cite{Friez} by C. Fries. We suggest to use the term Stochastic AAD in situations when expectation is an internal operation. We analyze this in detail and provide the cost estimate of the SAAD for the case when the AAD tool allows an automatic parallelization.

## Suggested Citation

• Dmitri Goloubentcev & Evgeny Lakshtanov, 2019. "Remarks on stochastic automatic adjoint differentiation and financial models calibration," Papers 1901.04200, arXiv.org.
• Handle: RePEc:arx:papers:1901.04200
as

File URL: http://arxiv.org/pdf/1901.04200

## References listed on IDEAS

as
1. Yuri F. Saporito & Xu Yang & Jorge P. Zubelli, 2017. "The Calibration of Stochastic-Local Volatility Models - An Inverse Problem Perspective," Papers 1711.03023, arXiv.org.
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