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Remarks on stochastic automatic adjoint differentiation and financial models calibration

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  • Dmitri Goloubentsev
  • 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 stochastic models. { We demonstrate that it allows a perfect SIMD\footnote{Single Input Multiple Data} parallelization and provide its relative computational cost. In addition we demonstrate that this theoretical result is in concordance with numeric experiments.}

Suggested Citation

  • Dmitri Goloubentsev & Evgeny Lakshtanov, 2019. "Remarks on stochastic automatic adjoint differentiation and financial models calibration," Papers 1901.04200, arXiv.org, revised Dec 2019.
    • Handle: RePEc:arx:papers:1901.04200
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    File URL: http://arxiv.org/pdf/1901.04200
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    References listed on IDEAS

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    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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    Cited by:

    1. Jos'e Brito & Andrei Goloubentsev & Evgeny Goncharov, 2022. "Automatic Adjoint Differentiation for special functions involving expectations," Papers 2204.05204, arXiv.org, revised Jan 2023.
    2. Evgeny Goncharov & Alexandre Rodrigues, 2022. "Modifications to a classic BFGS library for use with SIMD-equipped hardware and an AAD library," Papers 2209.14928, arXiv.org.

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