A critical analysis of the Weighted Least Squares Monte Carlo method for pricing American options

R. Mark Reesor, Lars Stentoft and Xiaotian Zhu

Finance Research Letters, 2024, vol. 64, issue C

Abstract: Least-squares Monte Carlo generates regression-based continuation value estimators that are heteroscedastic. Fabozzi et al. (2017) propose weighted least-squares regression to correct for this. We show that heteroscedastic-corrected estimators are more accurate than uncorrected estimators far from the exercise boundary and where the exercise decision is obvious. However, the corrected estimators do not translate into improved exercise decisions and hence correcting has little effect on option price estimates. This holds when using alternative specifications for the correction and when implementing an iterative method. We conclude that correcting for heteroscedasticity does not result in more efficient prices and generally should be avoided.

Keywords: American options; Heteroscedasticity corrections; Regression; Simulation (search for similar items in EconPapers)
JEL-codes: C12 C14 C22 C52 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:64:y:2024:i:c:s1544612324004094

DOI: 10.1016/j.frl.2024.105379

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