Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing

DOI10.1111/mafi.12405arXiv2202.02717MaRDI QIDQ6178392

Sebastian Becker, Arnulf Jentzen, Marvin S. Müller, Philippe von Wurstemberger

Publication date: 18 January 2024

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

Full work available at URL: https://arxiv.org/abs/2202.02717

zbMATH Keywords

Monte Carlo simulationBlack-Scholes modelquasi-Monte Carlo methodartificial neural networkEuropean call optionLRV strategystochastic gradient descent (SGD) optimization methodstochastic Lorentz equation

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Monte Carlo methods (65C05) Derivative securities (option pricing, hedging, etc.) (91G20) Numerical solutions to stochastic differential and integral equations (65C30)

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