# Self-similar martingales derived from Root embedding

## TL;DR

This paper characterizes when the Root embedding produces a self-similar martingale with specific marginals, linking the property to the monotonicity of the associated barriers and solutions, with examples and simulations.

## Contribution

It provides a necessary and sufficient condition for the Root embedding to generate self-similar martingales with scaled marginals, connecting barrier monotonicity to the embedding solutions.

## Key findings

- The Root embedding yields self-similar martingales under a specific monotonicity condition.
- The monotonicity of barriers is equivalent to the non-decreasing nature of the embedding times.
- Numerical simulations illustrate the barrier monotonicity in applicable cases.

## Abstract

Given a family $(\mu_\lambda,\lambda\geq0)$ of integrable mean-zero probability measures such that, for every $\lambda\geq0$, $\mu_\lambda$ is the image of $\mu_1$ under the homothety $y\longmapsto\sqrt{\lambda}y$, we provide a necessary and sufficient condition on $\mu_1$ under which the Root embedding algorithm yields a self-similar martingale with one-dimensional marginals $(\mu_\lambda,\lambda\geq0)$. Precisely, if $\tau_{\lambda}$ and $R_{\lambda}$ denote the Root solution to the Skorokhod embedding problem (SEP) and the Root regular barrier for $\mu_\lambda$ respectively, then this condition is equivalent to the property that $(R_{\lambda},\lambda\geq0)$ is non-increasing in the sense of inclusion, which in turn is equivalent to the assertion that $(\tau_\lambda,\lambda\geq0)$ is non-decreasing a.s. We show that there are many examples for which this result applies and we provide some numerical simulations to illustrate the monotonicity property of regular barriers $(R_{\lambda},\lambda\geq0)$ in this case.