Refining the least squares Monte Carlo method by imposing structure

DOI10.1080/14697688.2013.787543zbMath1294.91190OpenAlexW3125495861MaRDI QIDQ2879045

Lars Stentoft, P. Létourneau

Publication date: 5 September 2014

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

Full work available at URL: https://doi.org/10.1080/14697688.2013.787543

zbMATH Keywords

bias reductionAmerican optionssimulationsconstrained regression

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Monte Carlo methods (65C05) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (5)

An improved least squares Monte Carlo valuation method based on heteroscedasticity ⋮ Deep learning for ranking response surfaces with applications to optimal stopping problems ⋮ Optimal decision policy for real options under general Markovian dynamics ⋮ Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500 ⋮ Time-consistent and market-consistent actuarial valuation of the participating pension contract

Cites Work

• Convergence rates and asymptotic normality for series estimators
• On the robustness of least-squares Monte Carlo (LSM) for pricing American derivatives
• Discrete convexity: Convexity for functions defined on discrete spaces
• Regression-based algorithms for life insurance contracts with surrender guarantees
• Valuing American Options by Simulation: A Simple Least-Squares Approach
• Pricing and hedging American-style options: a simple simulation-based approach

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