Asymptotic lower bounds in estimating jumps

TL;DR

This paper establishes fundamental lower bounds for estimating jumps in stochastic processes from discrete data, proving the optimality of a threshold estimator through a convolution theorem and asymptotic analysis.

Contribution

It introduces a convolution theorem with explicit minimal variance for jump estimation and demonstrates the optimality of a threshold estimator in this context.

Findings

Lower bounds for jump estimation error are derived.

A convolution theorem with explicit minimal variance is established.

A threshold estimator achieves the minimal variance, proving optimality.

Abstract

We study the problem of the efficient estimation of the jumps for stochastic processes. We assume that the stochastic jump process $(X_t)_{t\in[0,1]}$ is observed discretely, with a sampling step of size $1/n$. In the spirit of Hajek's convolution theorem, we show some lower bounds for the estimation error of the sequence of the jumps $(\Delta X_{T_k})_k$. As an intermediate result, we prove a LAMN property, with rate $\sqrt{n}$, when the marks of the underlying jump component are deterministic. We deduce then a convolution theorem, with an explicit asymptotic minimal variance, in the case where the marks of the jump component are random. To prove that this lower bound is optimal, we show that a threshold estimator of the sequence of jumps $(\Delta X_{T_k})_k$ based on the discrete observations, reaches the minimal variance of the previous convolution theorem.