Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

DOI10.1080/14697688.2019.1701698zbMath1466.91339arXiv1905.09474OpenAlexW3003677889MaRDI QIDQ4991044

Antonino Zanette, Andrea Molent, Ludovic Goudenège

Publication date: 2 June 2021

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.09474

zbMATH Keywords

American optionsexact integrationmachine learningbinomial tree methodrough Bergomi modelmulti-dimensional Black-Scholes model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stopping times; optimal stopping problems; gambling theory (60G40) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (16)

Computational analysis of the behavior of stochastic volatility models with financial applications ⋮ Pricing Options under Rough Volatility with Backward SPDEs ⋮ Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms ⋮ Moving average options: machine learning and Gauss-Hermite quadrature for a double non-Markovian problem ⋮ Short-dated smile under rough volatility: asymptotics and numerics ⋮ Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities ⋮ JDOI variance reduction method and the pricing of American-style options ⋮ American Options in the Volterra Heston Model ⋮ KrigHedge: Gaussian Process Surrogates for Delta Hedging ⋮ A deep-genetic algorithm (deep-GA) approach for high-dimensional nonlinear parabolic partial differential equations ⋮ Local volatility under rough volatility ⋮ Applications of artificial neural networks to simulating Lévy processes ⋮ Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations ⋮ Deep Splitting Method for Parabolic PDEs ⋮ Gaussian process regression for pricing variable annuities with stochastic volatility and interest rate ⋮ DNN expression rate analysis of high-dimensional PDEs: application to option pricing

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