arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1209.3100math.PR
收藏

高斯粗糙微分方程解的密度光滑性

Smoothness of the density for solutions to Gaussian rough differential equations

Thomas Cass, Martin Hairer, Christian Litterer, Samy Tindel

摘要:

我们考虑由多维高斯过程驱动的形式为$dY_t=V(Y_t)\,dX_t+V_0(Y_t)\,dt$的随机微分方程。在矢量场$V_0$和$V=(V_1,\ldots,V_d)$满足Hörmander括号条件的假设下,我们证明了对于任何$t\in(0,T]$,只要驱动噪声满足一定的非简并性假设,$Y_t$都有光滑密度.我们的分析依赖于依赖于粗糙路径理论,Malliavin演算和高斯过程理论的相互作用。我们的结果适用于广泛的例子,包括分数布朗运动与赫斯特参数$H>1/4$,Ornstein-Uhlenbeck过程和布朗桥返回后的时间$T$。

英文摘要:

We consider stochastic differential equations of the form $dY_t=V(Y_t)\,dX_t+V_0(Y_t)\,dt$ driven by a multi-dimensional Gaussian process. Under the assumption that the vector fields $V_0$ and $V=(V_1,\ldots,V_d)$ satisfy Hörmander's bracket condition, we demonstrate that $Y_t$ admits a smooth density for any $t\in(0,T]$, provided the driving noise satisfies certain nondegeneracy assumptions. Our analysis relies on relies on an interplay of rough path theory, Malliavin calculus and the theory of Gaussian processes. Our result applies to a broad range of examples including fractional Brownian motion with Hurst parameter $H>1/4$, the Ornstein-Uhlenbeck process and the Brownian bridge returning after time $T$.

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。

↑