Dynamic programming for optimal stopping via pseudo-regression

DOI10.1080/14697688.2020.1780299zbMath1479.91389arXiv1808.04725OpenAlexW2963915950MaRDI QIDQ5014168

Martin Redmann, Christian Bayer, John G. M. Schoenmakers

Publication date: 1 December 2021

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

Full work available at URL: https://arxiv.org/abs/1808.04725

zbMATH Keywords

option pricingMonte Carlo simulationlinear regressionbasket optionsBermudan options

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Dynamic programming (90C39) Stopping times; optimal stopping problems; gambling theory (60G40) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (1)

Primal-Dual Regression Approach for Markov Decision Processes with General State and Action Spaces

Cites Work

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• Quantitative error estimates for a least-squares Monte Carlo algorithm for American option pricing
• A distribution-free theory of nonparametric regression
• Solving linear parabolic rough partial differential equations
• Advanced Simulation-Based Methods for Optimal Stopping and Control
• Optimal Stopping of McKean--Vlasov Diffusions via Regression on Particle Systems
• SDE Based Regression for Linear Random PDEs
• Valuing American Options by Simulation: A Simple Least-Squares Approach
• A QUANTIZATION TREE METHOD FOR PRICING AND HEDGING MULTIDIMENSIONAL AMERICAN OPTIONS
• Semitractability of optimal stopping problems via a weighted stochastic mesh algorithm

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