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Automatic Adjoint Differentiation for special functions involving expectations

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  • Jos'e Brito
  • Andrei Goloubentsev
  • Evgeny Goncharov

Abstract

We explain how to compute gradients of functions of the form $G = \frac{1}{2} \sum_{i=1}^{m} (E y_i - C_i)^2$, which often appear in the calibration of stochastic models, using Automatic Adjoint Differentiation and parallelization. We expand on the work of arXiv:1901.04200 and give faster and easier to implement approaches. We also provide an implementation of our methods and apply the technique to calibrate European options.

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  • Jos'e Brito & Andrei Goloubentsev & Evgeny Goncharov, 2022. "Automatic Adjoint Differentiation for special functions involving expectations," Papers 2204.05204, arXiv.org, revised Jan 2023.
    • Handle: RePEc:arx:papers:2204.05204
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    File URL: http://arxiv.org/pdf/2204.05204
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    References listed on IDEAS

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    1. Dmitri Goloubentsev & Evgeny Lakshtanov, 2019. "Remarks on stochastic automatic adjoint differentiation and financial models calibration," Papers 1901.04200, arXiv.org, revised Dec 2019.
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    Cited by:

    1. 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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    1. 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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