Minimax rates of convergence for the nonparametric estimation of the diffusion coefficient from time-homogeneous SDE paths

TL;DR

This paper investigates the optimal rates at which the square of the diffusion coefficient can be estimated nonparametrically from high-frequency, discrete observations of a time-homogeneous SDE, considering different observation schemes.

Contribution

It establishes the minimax convergence rates for nonparametric estimation of the diffusion coefficient's square under various observation schemes and function spaces.

Findings

Derived minimax convergence rates for the estimation problem.

Analyzed both single-path and multiple-path observation schemes.

Provided theoretical bounds for estimation accuracy in different settings.

Abstract

Consider a diffusion process X, solution of a time-homogeneous stochastic differential equation. We assume that the diffusion process X is observed at discrete times, at high frequency, which means that the time step tends toward zero. In addition, the drift and diffusion coefficients of the process X are assumed to be unknown. In this paper, we study the minimax rates of convergence of the nonparametric estimators of the square of the diffusion coefficient. Two observation schemes are considered depending on the estimation interval. The square of the diffusion coefficient is estimated on the real line from repeated observations of the process X, where the number of diffusion paths tends to infinity. For the case of a compact estimation interval, we study the nonparametric estimation of the square of the diffusion coefficient constructed from a single diffusion path on one side and from repeated observations on the other side, where the number of trajectories tends to infinity. In each of these cases, we establish minimax convergence rates of the risk of estimation of the diffusion coefficient over a space of Holder functions.