Convergence of a Least‐Squares Monte Carlo Algorithm for Bounded Approximating Sets

DOI10.1080/13504860802516881zbMath1169.91346OpenAlexW2083948485MaRDI QIDQ3395724

Daniel Z. Zanger

Publication date: 13 September 2009

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

Full work available at URL: https://doi.org/10.1080/13504860802516881

zbMATH Keywords

optimal stoppingAmerican optionsstatistical learningleast-squares Monte CarloLongstaff-Schwartz algorithm

Mathematics Subject Classification ID

Microeconomic theory (price theory and economic markets) (91B24)

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Monte-Carlo Valuation of American Options: Facts and New Algorithms to Improve Existing Methods ⋮ Quantitative error estimates for a least-squares Monte Carlo algorithm for American option pricing ⋮ Discrete-type approximations for non-Markovian optimal stopping problems. II ⋮ Primal–dual quasi-Monte Carlo simulation with dimension reduction for pricing American options ⋮ CONVERGENCE OF A LEAST‐SQUARES MONTE CARLO ALGORITHM FOR AMERICAN OPTION PRICING WITH DEPENDENT SAMPLE DATA ⋮ Pricing high-dimensional Bermudan options using the stochastic grid method ⋮ The homotopy perturbation method for the Black–Scholes equation ⋮ Pricing bounds and bang-bang analysis of the Polaris variable annuities ⋮ General Error Estimates for the Longstaff–Schwartz Least-Squares Monte Carlo Algorithm

Cites Work

• Monte Carlo algorithms for optimal stopping and statistical learning
• Sphere packing numbers for subsets of the Boolean \(n\)-cube with bounded Vapnik-Chervonenkis dimension
• An analysis of a least squares regression method for American option pricing
• On the mathematical foundations of learning
• Valuing American Options by Simulation: A Simple Least-Squares Approach
• Convergence of stochastic processes