ON THE ACCELERATION OF THE MULTI-LEVEL MONTE CARLO METHOD

被引：0

作者：

Debrabant, Kristian ^{[1
]}

Roessler, Andreas ^{[2
]}

机构：

[1] Univ So Denmark, Dept Math & Comp Sci, DK-5230 Odense, Denmark

[2] Univ Lubeck, Inst Math, D-23562 Lubeck, Germany

来源：

JOURNAL OF APPLIED PROBABILITY | 2015年 / 52卷 / 02期

关键词：

Multi-level Monte Carlo; Monte Carlo; weak approximation; variance reduction; stochastic differential equation; SIMULATION; SDES;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The multi-level Monte Carlo method proposed by Giles (2008) approximates the expectation of some functionals applied to a stochastic process with optimal order of convergence for the mean-square error. In this paper a modified multi-level Monte Carlo estimator is proposed with significantly reduced computational costs. As the main result, it is proved that the modified estimator reduces the computational costs asymptotically by a factor (p/alpha)(2) if weak approximation methods of orders alpha and p are applied in the case of computational costs growing with the same order as the variances decay.

引用

页码：307 / 322

页数：16

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