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arXiv 0706.2636math.PRcs.NAmath.NA
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分数布朗运动驱动的随机微分方程的最优点态逼近

Optimal pointwise approximation of stochastic differential equations driven by fractional Brownian motion

Johann Wolfgang Goethe-Universität Frankfurt(约翰·沃尔夫冈·歌德法兰克福大学)

Andreas Neuenkirch

AI总结:

针对Hurst参数H>1/2的分数布朗运动驱动的随机微分方程,研究了单点均方误差的最优收敛速率,并提出了达到该速率的可实现逼近方案。

摘要:

我们研究由Hurst参数$H>1/2$的分数布朗运动驱动的随机微分方程的逼近问题。对于单点均方误差,我们推导了使用驱动分数布朗运动的等距离散化的任何逼近方法所能达到的最优收敛速率。我们发现主要有两种情况:要么解可以被完美逼近,要么最佳可能收敛速率为$n^{-H-1/2}$,其中$n$表示分数布朗运动的评估次数。此外,我们提出了一种可实现的逼近方案,在后一种情况下达到了最优收敛速率。

英文摘要:

We study the approximation of stochastic differential equations driven by a fractional Brownian motion with Hurst parameter $H>1/2$. For the mean-square error at a single point we derive the optimal rate of convergence that can be achieved by any approximation method using an equidistant discretization of the driving fractional Brownian motion. We find that there are mainly two cases: either the solution can be approximated perfectly or the best possible rate of convergence is $n^{-H-1/2},$ where $n$ denotes the number of evaluations of the fractional Brownian motion. In addition, we present an implementable approximation scheme that obtains the optimal rate of convergence in the latter case.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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