Acceleration of quasi-Monte Carlo approximations with applications in mathematical finance

被引：1
|
作者：
Severino, JS [1 ]
Allen, EJ [1 ]
Victory, HD [1 ]
机构：
[1] Texas Tech Univ, Dept Math & Stat, Lubbock, TX 79409 USA
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2004年 / 148卷 / 01期
关键词：
quasi-Monte Carlo; acceleration; mathematical finance; computational algorithms;
D O I：
10.1016/S0096-3003(02)00836-6
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A procedure to accelerate the convergence of quasi-Monte Carlo approximations is described and investigated. The procedure applies a weighted least-squares smoothing method to improve the approximations that were obtained using low-discrepancy sequences. The acceleration procedure is tested computationally on several numerical integration problems including two problems of interest in mathematical finance. The results indicate that when Halton low-discrepancy points are used in the quasi-Monte Carlo method, the accelerated sequence often converges a factor of five or more faster than the original quasi-Monte Carlo approximations. (C) 2002 Elsevier Inc. All rights reserved.
引用
收藏
页码：173 / 187
页数：15
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