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

页码：173 / 187

页数：15

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