A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time

DOI10.1007/s10182-008-0067-0zbMath1477.62295OpenAlexW2094723276MaRDI QIDQ2006839

Yanyan Li

Publication date: 12 October 2020

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10182-008-0067-0

zbMATH Keywords

rate of convergenceconsistencyoptimal stoppingsmoothing splineAmerican optionsnonparametric regressionregression based Monte Carlo methods

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to actuarial sciences and financial mathematics (62P05) Numerical methods (including Monte Carlo methods) (91G60) Stopping times; optimal stopping problems; gambling theory (60G40) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (4)

Pricing of American options in discrete time using least squares estimates with complexity penalties ⋮ Analysis of least squares regression estimates in case of additional errors in the variables ⋮ Regression Monte Carlo for impulse control ⋮ Solving high-dimensional optimal stopping problems using deep learning

Cites Work

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• Monte Carlo algorithms for optimal stopping and statistical learning
• A new approach to least-squares estimation, with applications
• Application of structural risk minimization to multivariate smoothing spline regression estimates
• Estimating a regression function
• MaxWeight scheduling in a generalized switch: State space collapse and workload minimization in heavy traffic
• Optimal global rates of convergence for nonparametric regression
• A distribution-free theory of nonparametric regression
• A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options
• Nonparametric regression with additional measurement errors in the dependent variable
• PRICING OF HIGH-DIMENSIONAL AMERICAN OPTIONS BY NEURAL NETWORKS
• Necessary and sufficient conditions for the pointwise convergence of nearest neighbor regression function estimates
• Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives
• Nonparametric regression estimation using penalized least squares
• Valuing American Options by Simulation: A Simple Least-Squares Approach
• Estimating market risk with neural networks

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