Deep learning-based least squares forward-backward stochastic differential equation solver for high-dimensional derivative pricing

Jian Liang, Zhe Xu and Peter Li

Quantitative Finance, 2021, vol. 21, issue 8, 1309-1323

Abstract: We propose a new forward-backward stochastic differential equation solver for high-dimensional derivative pricing problems by combining a deep learning solver with a least squares regression technique widely used in the least squares Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the accuracy of our least squares backward deep neural network solver and its capability to produce accurate prices for complex early exercisable derivatives, such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an accurate means for pricing high-dimensional derivatives with early exercise features.

Date: 2021
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DOI: 10.1080/14697688.2021.1881149

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