Variance-constrained canonical least-squares Monte Carlo: An accurate method for pricing American options

摘要

The pricing accuracy of the canonical least-squares Monte Carlo (CLM) method can be improved significantly by incorporating inno-vatively a variance constraint in the derivation of the canonical risk-neutral distribution. This new approach is called the variance-constrained CLM (vCLM) in the paper. Operationally, the forward variance is set to be the square of the volatility implied under vCLM by the option's market price from a previous trading day. For 16,249 American-style S&P 100 index puts, vCLM produced an average absolute pricing error of 5.94%, easily outperforming CLM, a competing nonparametric approach, and a GARCH-based benchmark.