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.

中文翻译：

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美式期权定价加权最小二乘蒙特卡罗方法的批判性分析

最小二乘蒙特卡罗生成基于回归的异方差连续值估计量。法博齐等人。 （2017）提出加权最小二乘回归来纠正这一点。我们表明，异方差校正估计器比远离练习边界且练习决策明显的未校正估计器更准确。然而，修正后的估计量并不能转化为改进的行权决策，因此修正对期权价格估计影响不大。当使用替代规范进行校正和实施迭代方法时，这一点成立。我们的结论是，校正异方差不会导致更有效的价格，通常应该避免。