An Analysis of Weighted Least Squares Monte Carlo
Abstract
Since Longstaff and Schwartz [2001] brought the amazing Regression-based Monte Carlo (LSMC) method in pricing American options, it has received heated discussion. Based on the research done by Fabozzi et al. [2017] that applies the heteroscedasticity correction method to LSMC, we further extend the study by introducing the methods from Park [1966] and Harvey [1976]. Our work shows that for a single stock American Call option modelled by GBM with two exercise opportunities, WLSMC or IRLSMC provides better estimates in continuation value than LSMC. However, they do not lead to better exercise decisions and hence have little to no effect on option price estimates. Our work finally indicates that in terms of real-life options pricing modelled by univariate GBM, bivariate GBM, and univariate GARCH, WLSMC or IRLSMC are not effective at producing more efficient price estimates .
Key Words: [LSMC, Regression, Heteroscedasticity Correction].