# Estimation of Stopping Times for Stopped Self-Similar Random Processes

## TL;DR

This paper develops a non-parametric method to estimate the distribution of an unknown stopping time for self-similar processes, extending previous work on Brownian motion to broader classes like Bessel processes, with proven convergence rates and asymptotic properties.

## Contribution

It introduces a new estimator for the stopping time distribution of self-similar processes, generalizing Mellin transform techniques beyond Brownian motion.

## Key findings

- Derived the minimax convergence rate for the estimator.
- Established asymptotic normality for Bessel processes.
- Extended estimation methods to a wider class of self-similar processes.

## Abstract

Let $X=(X_t)_{t\geq 0}$ be a known process and $T$ an unknown random time independent of $X$. Our goal is to derive the distribution of $T$ based on an iid sample of $X_T$. Belomestny and Schoenmakers (2015) propose a solution based the Mellin transform in case where $X$ is a Brownian motion. Applying their technique we construct a non-parametric estimator for the density of $T$ for a self-similar one-dimensional process $X$. We calculate the minimax convergence rate of our estimator in some examples with a particular focus on Bessel processes where we also show asymptotic normality.