Pricing of American options in discrete time using least squares estimates with complexity penalties

DOI10.1016/j.jspi.2012.02.031zbMath1244.62149OpenAlexW2059927071MaRDI QIDQ433745

Adam Krzyżak, Michael Kohler

Publication date: 6 July 2012

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2012.02.031

zbMATH Keywords

rate of convergenceneural networksoptimal stoppingsmoothing splinesnonparametric regressionorthogonal series estimatesregression based Monte Carlo methods

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Monte Carlo methods (65C05) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (5)

Second-order integro-differential parabolic variational inequalities arising from the valuation of American option ⋮ Smoothing spline regression estimation based on real and artificial data ⋮ Estimation of a density using an improved surrogate model ⋮ ON THE CONSISTENCY OF REGRESSION‐BASED MONTE CARLO METHODS FOR PRICING BERMUDAN OPTIONS IN CASE OF ESTIMATED FINANCIAL MODELS ⋮ Solving high-dimensional optimal stopping problems using deep learning

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