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Simulation for American Options: Regression Now or Regression Later?

In: Monte Carlo and Quasi-Monte Carlo Methods 2002

Author

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  • Paul Glasserman

    (Graduate School of Business, Columbia University)

  • Bin Yu

    (Graduate School of Business, Columbia University)

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.

Suggested Citation

  • Paul Glasserman & Bin Yu, 2004. "Simulation for American Options: Regression Now or Regression Later?," Springer Books, in: Harald Niederreiter (ed.), Monte Carlo and Quasi-Monte Carlo Methods 2002, pages 213-226, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-18743-8_12
    DOI: 10.1007/978-3-642-18743-8_12
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