Generalized arcsine laws for fractional Brownian motion

Tridib Sadhu, Mathieu Delorme, Kay Jörg Wiese

Published 2017-06-06Version 1

The three arcsine laws for Brownian motion are a cornerstone of extreme-value statistics. For a Brownian $B_t$ starting from the origin, and evolving during time $T$, one considers the following three observables: (i) the duration $t_+$ the process is positive, (ii) the time $t_{\rm last}$ the process last visits the origin, and (iii) the time $t_{\rm max}$ when it achieves its maximum (or minimum). All three observables have the same cumulative probability distribution expressed as an arcsine function, thus the name of arcsine laws. We show how these laws change for fractional Brownian motion $X_t$, a non-Markovian Gaussian process indexed by the Hurst exponent $H$. It generalizes standard Brownian motion (i.e. $H=\tfrac{1}{2}$). We obtain the three probabilities using a perturbative expansion in $\epsilon = H-\tfrac{1}{2}$. While all three probabilities are different, this distinction can only be made at second order in $\epsilon$. Our results are confirmed to high precision by extensive numerical simulations.