Simultaneous upper and lower bounds of American-style option prices with hedging via neural networks

Ivan Guo, Nicolas Langren\'e and Jiahao Wu

Papers from arXiv.org

Abstract: In this paper, we introduce two novel methods to solve the American-style option pricing problem and its dual form at the same time using neural networks. Without applying nested Monte Carlo, the first method uses a series of neural networks to simultaneously compute both the lower and upper bounds of the option price, and the second one accomplishes the same goal with one global network. The avoidance of extra simulations and the use of neural networks significantly reduce the computational complexity and allow us to price Bermudan options with frequent exercise opportunities in high dimensions, as illustrated by the provided numerical experiments. As a by-product, these methods also derive a hedging strategy for the option, which can also be used as a control variate for variance reduction.

Date: 2023-02, Revised 2024-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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