Neural network regression for Bermudan option pricing

DOI10.1515/mcma-2021-2091zbMath1476.62205arXiv1907.06474OpenAlexW3176021757MaRDI QIDQ2239248

Jérôme Lelong, Bernard Lapeyre

Publication date: 3 November 2021

Published in: Monte Carlo Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1907.06474

zbMATH Keywords

neural networksoptimal stoppingdeep learningregression methodsBermuda options

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Numerical methods (including Monte Carlo methods) (91G60) Artificial neural networks and deep learning (68T07) Stochastic approximation (62L20) Stopping times; optimal stopping problems; gambling theory (60G40) Parallel numerical computation (65Y05) Optimal stopping in statistics (62L15) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (8)

American Options in the Volterra Heston Model ⋮ Pricing Bermudan Options Using Regression Trees/Random Forests ⋮ A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations ⋮ Convergence of the Backward Deep BSDE Method with Applications to Optimal Stopping Problems ⋮ Pricing High-Dimensional Bermudan Options with Hierarchical Tensor Formats ⋮ Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing ⋮ Explainable neural network for pricing and universal static hedging of contingent claims ⋮ Solving High-Dimensional Optimal Stopping Problems Using Optimization Based Model Order Reduction

Uses Software

• Scikit
• TensorFlow

Cites Work

• Multi-asset American options and parallel quantization
• Pricing early-exercise and discrete barrier options by Fourier-cosine series expansions
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• Number of paths versus number of basis functions in American option pricing
• Kolmogorov width decay and poor approximators in machine learning: shallow neural networks, random feature models and neural tangent kernels
• A regression-based Monte Carlo method to solve backward stochastic differential equations
• Monte-Carlo Valuation of American Options: Facts and New Algorithms to Improve Existing Methods
• PRICING OF HIGH-DIMENSIONAL AMERICAN OPTIONS BY NEURAL NETWORKS
• Pricing American Options: A Duality Approach
• Optimization Methods for Large-Scale Machine Learning
• Solving high-dimensional optimal stopping problems using deep learning
• A Fast and Accurate FFT-Based Method for Pricing Early-Exercise Options under Lévy Processes
• Deep optimal stopping
• Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming
• Approximation by superpositions of a sigmoidal function
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