mlsanityData to 7 Oct 2026

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

arXiv 2302.12439 ↗First posted 24 Feb 2023q-fin.CP math.PR stat.MLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 62 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

13 of 62 references.

References · 13

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  7. 2018Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysiscited by 22
  8. 2019Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensionscited by 14
  9. 2022Convergence of the Backward Deep BSDE Method with Applications to Optimal Stopping Problemscited by 9
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  12. 2023Efficient Pricing and Hedging of High Dimensional American Options Using Recurrent Networkscited by 2
1 more
  1. 2015Kriging Metamodels and Experimental Design for Bermudan Option Pricingcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Deep Learning for the Multiple Optimal Stopping Problemcited by 1
  2. 2025DeepMartingale: Duality of the Optimal Stopping Problem with Expressivity and High-Dimensional Hedgingcited by 0
  3. 2026Continuous-time Optimal Stopping through Deep Reinforcement Learningcited by 0