Automatic control variates for option pricing using neural networks

DOI10.1515/mcma-2020-2081zbMath1469.91063OpenAlexW3121624243MaRDI QIDQ2040464

Jérôme Lelong, Zineb El Filali Ech-Chafiq, Adil Reghai

Publication date: 14 July 2021

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

Full work available at URL: https://hal.univ-grenoble-alpes.fr/hal-02891798/file/Automatic_Control_variates_for_option_pricing__using_neural_networks_.pdf

zbMATH Keywords

neural networksMonte Carlo methodvariance reductioneffective dimensioncontrol variate

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Artificial neural networks and deep learning (68T07) Monte Carlo methods (65C05) Derivative securities (option pricing, hedging, etc.) (91G20)

Uses Software

• Adam

Cites Work

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• Robust adaptive numerical integration of irregular functions with applications to basket and other multi-dimensional exotic options
• Efficient Monte Carlo pricing of European options using mean value control variates
• Coupling importance sampling and multilevel Monte Carlo using sample average approximation
• The effective dimension and quasi-Monte Carlo integration
• A framework for adaptive Monte Carlo procedures
• Monte Carlo integration with a growing number of control variates
• Adaptive Control Variates for Finite-Horizon Simulation
• Why Are High-Dimensional Finance Problems Often of Low Effective Dimension?
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

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