Adaptive Optimal Allocation in Stratified Sampling Methods
Adaptive Optimal Allocation in Stratified Sampling Methods
复制标题
分层抽样方法中的自适应优化分配
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
B. Jourdain
中科院分区：
文献类型：
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作者：
P. Étoré;B. Jourdain
In this paper, we propose a stratified sampling algorithm in which the random drawings made in the strata to compute the expectation of interest are also used to adaptively modify the proportion of further drawings in each stratum. These proportions converge to the optimal allocation in terms of variance reduction and our stratified estimator is asymptotically normal with asymptotic variance equal to the minimal one. Numerical experiments confirm the efficiency of our algorithm. For the pricing of arithmetic average Asian options in the Black and Scholes model, the variance is divided by a factor going from 1.1 to 50.4 (depending on the option type and the moneyness) in comparison with the standard allocation procedure, while the increase in computation time does not overcome 1%.
