What question did this study set out to answer?

To develop a simple and computationally efficient simulation-based framework using least squares to value American-style options in complex, multifactor environments.

January 1, 2001Review of Financial Studies

Valuing American Options by Simulation: A Simple Least-Squares Approach

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To develop a simple and computationally efficient simulation-based framework using least squares to value American-style options in complex, multifactor environments.
Employed least-squares regression within Monte Carlo simulation paths to estimate the conditional expected payoff of continuing to hold an option.
Tested the methodology on path-dependent and high-dimensional financial problems where standard finite difference techniques fail.
Evaluated performance using benchmark examples, including an asset following a jump-diffusion process and an American swaption modeled with a 20-factor string framework.
Demonstrated that least-squares regression effectively estimates continuation values across simulated paths without requiring numerical grids.
Successfully priced American-style contracts in high-dimensional settings, including a 20-factor term structure model, overcoming the curse of dimensionality.

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Implication

Simulation study demonstrates efficient valuation of American options in complex multifactor settings, highlighting a practical alternative to traditional finite difference methods.

Cite This Study

Longstaff et al. (2001) studied this question.

synapsesocial.com/papers/6a0fcf8d8090e499da603405 https://doi.org/10.1093/rfs/14.1.113

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Implication

Simulation study demonstrates efficient valuation of American options in complex multifactor settings, highlighting a practical alternative to traditional finite difference methods.

Valuing American Options by Simulation: A Simple Least-Squares Approach

Key Points

Discussion

Implication

Cite This Study

Valuing American Options by Simulation: A Simple Least-Squares Approach

Key Points

Discussion

Implication

Cite This Study

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