P. Glasserman, P. Heidelberger and P. Shahabuddin, “Asymptotically Optimal Importance Sampling and Stratification for Pricing Path-Dependent Options,” Mathematical Finance, Vol. 9, No. 2, 1999, pp. 117-152.
http://dx.doi.org/10.1111/1467-9965.00065

has been cited by the following article:

• TITLE: Variance Reduction Techniques of Importance Sampling Monte Carlo Methods for Pricing Options

  AUTHORS: Qiang Zhao, Guo Liu, Guiding Gu

  KEYWORDS: Monte Carlo Method; Importance Sampling; Variance Reduction; Option Pricing

  JOURNAL NAME: Journal of Mathematical Finance, Vol.3 No.4, October 17, 2013

  ABSTRACT: In this paper we discuss the importance sampling Monte Carlo methods for pricing options. The classical importance sampling method is used to eliminate the variance caused by the linear part of the logarithmic function of payoff. The variance caused by the quadratic part is reduced by stratified sampling. We eliminate both kinds of variances just by importance sampling. The corresponding space for the eigenvalues of the Hessian matrix of the logarithmic function of payoff is enlarged. Computational Simulation shows the high efficiency of the new method.