Asymptotic behavior of a weighted self-repelling diffusion driven by fractional Brownian motion

Yaqin Sun , Hongfei Xue , Litan Yan

Probability, Uncertainty and Quantitative Risk ›› 2024, Vol. 9 ›› Issue (4) : 575 -604.

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Probability, Uncertainty and Quantitative Risk ›› 2024, Vol. 9 ›› Issue (4) : 575 -604. DOI: 10.3934/puqr.2024024
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Asymptotic behavior of a weighted self-repelling diffusion driven by fractional Brownian motion

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Abstract

Let ${B}^{H}$ be a fractional Brownian motion with Hurst index $\frac{1}{2}\le H<1$. In this paper, we consider the self-repelling diffusion ${X}_{t}^{H}={B}_{t}^{H}+\underset{0}{\overset{t}{{\displaystyle \int }}}\underset{0}{\overset{s}{{\displaystyle \int }}}g(u)\text{d}{X}_{u}^{H}\text{d}s+\nu t$,where $\nu \in ℝ$, and $g$ is a nonnegative Borel function. The process is an analogue of linear self-interacting diffusion (M. Cranston and Y. Le Jan Math. Ann. 303 (1995), 87-93.). Based on the asymptotic behavior of the weight function $g$ at infinity, we establish the large time behavior of the recursive convergence of the solution ${X}^{H}$. For example, when $g\in {C}^{\infty }({ℝ}_{+})$ and $0<g(t) \rightarrow+\infty(t \rightarrow+\infty)$, we demonstrate that there is a sequence $\left\{{\lambda }_{n}\right\}$ of positive real numbers such that $J_{t}^{H}(0 ; g):=g(t) e^{-G(t)} X_{t}^{H} \rightarrow \nu+\xi_{\infty}^{H}$ and $J_{t}^{H}(n ; g):=G(t)\left(J_{t}^{H}(n-1 ; g)-\lambda_{n-1}\left(\xi_{\infty}^{H}+\nu\right)\right) \longrightarrow \lambda_{n}\left(\xi_{\infty}^{H}+\nu\right) \quad(t \rightarrow+\infty)$ in ${L}^{2}$ and almost surely for every $n\in \{1,2,\dots \}$, where $G(t)=\underset{0}{\overset{t}{{\displaystyle \int }}}g(s)\text{d}s$ and $\xi_{\infty}^{H}:=\int_{0}^{\infty} g(r) e^{-G(r)} \mathrm{d} B_{r}^{H}$.

Keywords

Fractional brownian motion / Self-repelling diffusion / Malliavin calculus / Recursive convergence

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Yaqin Sun, Hongfei Xue, Litan Yan. Asymptotic behavior of a weighted self-repelling diffusion driven by fractional Brownian motion. Probability, Uncertainty and Quantitative Risk, 2024, 9(4): 575-604 DOI:10.3934/puqr.2024024

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Acknowledgements

The authors would like to thank the editor and the anonymous referee for their valuable suggestions and comments. The research is supported by the National Natural Science Foundation of China (Grant No. 11971101).

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