Work

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

Year: 2025

Type: article

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.

Source: Quantitative Finance

DOI: 10.1080/14697688.2025.2472874

Authors: Ivan Guo, Nicolas Langrené, Jiahao Wu

Institutions: Monash University, Beijing Normal-Hong Kong Baptist University

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

Funders: Commonwealth Scientific and Industrial Research Organisation, Hong Kong Baptist University, Australian Research Council

Awards: G2268825060, Can green investors drive the transition to a low emissions economy?