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arXiv · 2204.05204

Automatic Adjoint Differentiation for special functions involving expectations

José Brito·Andrei Goloubentsev·Evgeny Goncharov
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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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José Brito, Andrei Goloubentsev, Evgeny Goncharov. 2023-01-24. Automatic Adjoint Differentiation for special functions involving expectations. https://arxiv.org/abs/2204.05204

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