Strong approximation of fractional Brownian motion by moving averages of simple random walks
Tamas Szabados

TL;DR
This paper presents a method to approximate fractional Brownian motion using moving averages of simple random walks, achieving almost sure uniform convergence with explicit convergence rates, extending classical Brownian motion approximations.
Contribution
It introduces a novel approximation technique for fractional Brownian motion via moving averages of nested simple random walks, with proven convergence rates for Hurst parameter H in (1/4, 1).
Findings
Almost sure uniform convergence on compacts for H in (1/4, 1)
Convergence rate of O(N^{- ext{min}(H-1/4, 1/4)} ext{log} N)
Extension to all H in (0, 1) with conjectured optimal rate O(N^{-H} ext{log} N)
Abstract
The fractional Brownian motion is a generalization of ordinary Brownian motion, used particularly when long-range dependence is required. Its explicit introduction is due to B.B. Mandelbrot and J.W. van Ness (1968) as a self-similar Gaussian process with stationary increments. Here self-similarity means that , where is the Hurst parameter of fractional Brownian motion. F.B. Knight gave a construction of ordinary Brownian motion as a limit of simple random walks in 1961. Later his method was simplified by P. R\'ev\'esz (1990) and then by the present author (1996). This approach is quite natural and elementary, and as such, can be extended to more general situations. Based on this, here we use moving averages of a suitable nested sequence of simple random walks that almost surely uniformly converge to…
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