Simulation for American Options: Regression Now or Regression Later?

Paul Glasserman () and Bin Yu ()
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Paul Glasserman: Graduate School of Business, Columbia University
Bin Yu: Graduate School of Business, Columbia University

A chapter in Monte Carlo and Quasi-Monte Carlo Methods 2002, 2004, pp 213-226 from Springer

Abstract: Summary Pricing American options requires solving an optimal stopping problem and therefore presents a challenge for simulation. This article investigates connections between a weighted Monte Carlo technique and regression-based methods for this problem. The weighted Monte Carlo technique is shown to be equivalent to a least-squares method in which option values are regressed at a later time than in other regression-based methods. This “regression later” technique is shown to have two attractive features: under appropriate conditions, (i) it results in less-dispersed estimates, and (ii) it provides a dual estimate (an upper bound) with modest additional effort. These features result, more generally, from using martingale regressors.

Keywords: Basis Function; Conditional Expectation; American Option; Approximate Dynamic Program; Martingale Property (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-18743-8_12

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DOI: 10.1007/978-3-642-18743-8_12

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