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  • 标题:Variance Reduction Techniques of Importance Sampling Monte Carlo Methods for Pricing Options
  • 本地全文:下载
  • 作者:Qiang Zhao ; Guo Liu ; Guiding Gu
  • 期刊名称:Journal of Mathematical Finance
  • 印刷版ISSN:2162-2434
  • 电子版ISSN:2162-2442
  • 出版年度:2013
  • 卷号:03
  • 期号:04
  • 页码:431-436
  • DOI:10.4236/jmf.2013.34045
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要: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.
  • 关键词:Monte Carlo Method; Importance Sampling; Variance Reduction; Option Pricing
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