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arXiv 2512.16396math.PRcs.LGq-fin.MF

具有布朗签名的全局通用逼近

Global universal approximation with Brownian signatures

University of Mannheim(曼海姆大学)

Mihriban Ceylan, David J. Prömel

AI总结:

研究在合适粗糙路径空间上一般路径依赖和非预期泛函的\(L^p\)通用逼近定理,通过推导加权粗糙路径空间的全局通用逼近定理,证明其适用于高斯过程,可逼近布朗滤波适应的\(p\)可积随机过程。

摘要:

我们在合适的粗糙路径空间上为一般路径依赖和非预期泛函建立了\(L^p\)通用逼近定理,表明作用于时间扩展粗糙路径签名的线性泛函在\(L^p\)距离下是稠密的。为此,我们推导了加权粗糙路径空间的全局通用逼近定理。我们证明这些\(L^p\)通用逼近定理适用于高斯过程,特别是分数布朗运动。因此,时间扩展布朗运动签名上的线性泛函可以逼近任何适应布朗滤波的\(p\)可积随机过程,包括随机微分方程的解。

英文摘要:

We establish $L^p$-universal approximation theorems for general path-dependent and non-anticipative functionals on suitable rough path spaces, showing that linear functionals acting on signatures of time-extended rough paths are dense with respect to the $L^p$-distance. To that end, we derive global universal approximation theorems for weighted rough path spaces. We demonstrate that these $L^p$-universal approximation theorems apply to Gaussian processes, in particular, to fractional Brownian motion. As a consequence, linear functionals on the signature of the time-extended Brownian motion can approximate any $p$-integrable stochastic process adapted to the Brownian filtration, including solutions to stochastic differential equations.

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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