Doubly stochastic continuous time random walk

TL;DR

This paper introduces a doubly stochastic continuous time random walk model that incorporates fluctuating jump rates, enabling it to describe complex diffusion phenomena like Brownian yet non-Gaussian diffusion observed in various systems.

Contribution

It extends the classical Montroll-Weiss model by adding a stochastic layer for jump rates, enhancing its ability to model real-world anomalous diffusion effects.

Findings

The model captures rich diffusion behaviors including Brownian yet non-Gaussian diffusion.

It remains fully tractable despite added complexity.

Provides an alternative explanation to diffusing diffusivity phenomena.

Abstract

Since its introduction, some sixty years ago, the Montroll-Weiss continuous time random walk has found numerous applications due its ease of use and ability to describe both regular and anomalous diffusion. Yet, despite its broad applicability and generality, the model cannot account for effects coming from random diffusivity fluctuations which have been observed in the motion of asset prices and molecules. To bridge this gap, we introduce a doubly stochastic version of the model in which waiting times between jumps are replaced with a fluctuating jump rate. We show that this newly added layer of randomness gives rise to a rich phenomenology while keeping the model fully tractable -- allowing us to explore general properties and illustrate them with examples. In particular, we show that the model presented herein provides an alternative pathway to Brownian yet non-Gaussian diffusion which has been observed and explained via diffusing diffusivity approaches.