Fractional multiplicative processes
Julien Barral, Benoit Mandelbrot

TL;DR
This paper extends multiplicative cascade models by allowing negative weights, resulting in martingales that converge to self-similar functions with properties akin to fractional Brownian motion, revealing new stable laws and divergence behaviors.
Contribution
It introduces a novel class of self-similar processes via negative weights in multiplicative cascades, linking them to fractional Brownian motion and stable laws.
Findings
For H in (1/2,1), the process converges to a self-similar function with fractional Brownian motion properties.
For H in (0,1/2], the process diverges but normalized versions converge to Brownian motion.
New stable law type emerges, stable under random weighted averaging, satisfying a functional equation.
Abstract
Statistically self-similar measures on are limit of multiplicative cascades of random weights distributed on the -adic subintervals of . These weights are i.i.d, positive, and of expectation . We extend these cascades naturally by allowing the random weights to take negative values. This yields martingales taking values in the space of continuous functions on . Specifically, we consider for each the martingale obtained when the weights take the values and , in order to get converging almost surely uniformly to a statistically self-similar function whose H\"{o}lder regularity and fractal properties are comparable with that of the fractional Brownian motion of exponent . This indeed holds when . Also the construction introduces a new kind of law, one that it is stable under random…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · advanced mathematical theories · Mathematical Dynamics and Fractals
