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arXiv 1605.03475math.PR

由分数布朗运动驱动的随机微分方程泛函在Hurst参数上的Lipschitz连续性

Lipschitz continuity in the Hurst parameter of functionals of stochastic differential equations driven by a fractional Brownian motion

Alexandre Richard, Denis Talay

AI总结:

本文研究了由分数布朗运动驱动的随机微分方程解的泛函在Hurst参数趋近临界值1/2时的敏感性,证明了期望平滑函数和首达时间Laplace变换的Lipschitz连续性,表明此时Markov Brownian模型是良好代理。

摘要:

针对由分数布朗运动驱动的随机微分方程解的泛函,其概率分布关于长程/记忆噪声参数的敏感性分析是许多应用中重要的随机建模问题。本文考虑由分数布朗运动驱动的随机微分方程的解$\{X^H_t\}_{t\in \mathbb{R}_+}$。当噪声的Hurst参数$H$从上方或下方趋近于临界Brownian参数$H=\tfrac{1}{2}$时,我们开发了两种创新的敏感性分析。首先,我们在固定时间范围$T$下考察$X^H$的期望平滑函数。其次,我们考察关于Malliavin计算不规则的泛函的Laplace变换,即$X^H$在给定阈值处的首达时间。在两种情况下,我们都展示了在$\tfrac{1}{2}$附近关于$H$的Lipschitz连续性。因此,我们的结果表明,只要Hurst参数保持接近$\tfrac{1}{2}$,Markov Brownian模型就是一个良好的代理模型。

英文摘要:

Sensitivity analysis w.r.t. the long-range/memory noise parameter for probability distributions of functionals of solutions to stochastic differential equations is an important stochastic modeling issue in many applications. In this paper we consider solutions $\{X^H_t\}_{t\in \mathbb{R}_+}$ to stochastic differential equations driven by fractional Brownian motions. We develop two innovative sensitivity analyses when the Hurst parameter $H$ of the noise tends to the critical Brownian parameter $H=\tfrac{1}{2}$ from above or from below. First, we examine expected smooth functions of $X^H$ at a fixed time horizon $T$. Second, we examine Laplace transforms of functionals which are irregular with regard to Malliavin calculus, namely, first passage times of $X^H$ at a given threshold. In both cases we exhibit the Lipschitz continuity w.r.t. $H$ around the value $\tfrac{1}{2}$. Therefore, our results show that the Markov Brownian model is a good proxy model as long as the Hurst parameter remains close to $\tfrac{1}{2}$.

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