CONVERGENCE OF A LEAST‐SQUARES MONTE CARLO ALGORITHM FOR AMERICAN OPTION PRICING WITH DEPENDENT SAMPLE DATA

DOI10.1111/mafi.12125zbMath1403.91354OpenAlexW2413747182MaRDI QIDQ4635047

Daniel Z. Zanger

Publication date: 13 April 2018

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

Full work available at URL: https://doi.org/10.1111/mafi.12125

zbMATH Keywords

optimal stoppingAmerican optionsMonte Carlo algorithmsstatistical learning theoryleast-squares regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05) Stopping times; optimal stopping problems; gambling theory (60G40) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (10)

Variance reduction for risk measures with importance sampling in nested simulation ⋮ Options as silver bullets: valuation of term loans, inventory management, emissions trading and insurance risk mitigation using option theory ⋮ Nonexact oracle inequalities, \(r\)-learnability, and fast rates ⋮ Discrete-type approximations for non-Markovian optimal stopping problems. II ⋮ Deep-Learning Solution to Portfolio Selection with Serially Dependent Returns ⋮ Discrete-type approximations for non-Markovian optimal stopping problems: Part I ⋮ Pricing American options by exercise rate optimization ⋮ AMERICAN OPTION PRICING WITH REGRESSION: CONVERGENCE ANALYSIS ⋮ General Error Estimates for the Longstaff–Schwartz Least-Squares Monte Carlo Algorithm ⋮ Implied stopping rules for American basket options from Markovian projection

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