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Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights · Study

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ChapterPricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights

Mark N. Broadie2000

Chapter2000

Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights

Mark N. Broadie · Paul Glasserman · Zachary Ha

49 citations·0/0 files stored·Nonconvex optimization and its applications

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Stochastic processes and financial applications Simulation Techniques and Applications Mathematical Approximation and Integration

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References

2001Valuing American Options by Simulation: A Simple Least-Squares Approach 1997Pricing American-style securities using simulation 1996Valuation of the early-exercise price for options using simulations and nonparametric regression 2004A stochastic mesh method for pricing high-dimensional American options 1999Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives 1995Numerical Valuation of High Dimensional Multivariate American Securities 1997Calibrating volatility surfaces via relative-entropy minimization 2000A simple approach to the pricing of Bermudan swaptions in the multifactor LIBOR market model

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2004A stochastic mesh method for pricing high-dimensional American options 2006An iterative method for multiple stopping: convergence and stability 2005Large Sample Properties of Weighted Monte Carlo Estimators 2012Pricing high-dimensional Bermudan options using the stochastic grid method 2001Valuation of the Reset Options Embedded in Some Equity-Linked Insurance Products

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Year2000

Typechapter

Published2000-01-01

VenueNonconvex optimization and its applications

ISSN-L1571-568X

Languageen

Citations49

Files0 public / 0

ShelfStochastic processes and financial applications

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doi10.1007/978-1-4757-3150-7_2

openalex_workW1574770694

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2010Naïve learning algorithms utilized for the prediction of stock prices to compare economic models of decision making