Estimation of the invariant density for discretely observed diffusion processes: impact of the sampling and of the asynchronicity

TL;DR

This paper develops a non-parametric kernel density estimator for the invariant density of multi-dimensional diffusion processes from discretely observed data, analyzing its convergence rates under various sampling regimes and asynchronicity.

Contribution

It introduces optimal convergence rates for density estimation from discretely sampled diffusion processes, including intermediate and asynchronous observation regimes.

Findings

Optimal rates achieved under certain discretization conditions.

Convergence rates match those of iid density estimation in the intermediate regime.

Asynchronicity introduces additional bias but manageable variance.

Abstract

We aim at estimating in a non-parametric way the density $\pi$ of the stationary distribution of a $d$-dimensional stochastic differential equation $(X_t)_{t \in [0, T]}$, for $d \ge 2$, from the discrete observations of a finite sample $X_{t_0}$, ... , $X_{t_n}$ with $0= t_0 < t_1 < ... < t_n =: T_n$. We propose a kernel density estimator and we study its convergence rates for the pointwise estimation of the invariant density under anisotropic H\"older smoothness constraints. First of all, we find some conditions on the discretization step that ensures it is possible to recover the same rates as if the continuous trajectory of the process was available. Such rates are optimal and new in the context of density estimator. Then we deal with the case where such a condition on the discretization step is not satisfied, which we refer to as intermediate regime. In this new regime we identify the convergence rate for the estimation of the invariant density over anisotropic H\"older classes, which is the same convergence rate as for the estimation of a probability density belonging to an anisotropic H\"older class, associated to $n$ iid random variables $X_1, ..., X_n$. After that we focus on the asynchronous case, in which each component can be observed at different time points. Even if the asynchronicity of the observations complexifies the computation of the variance of the estimator, we are able to find conditions ensuring that this variance is comparable to the one of the continuous case. We also exhibit that the non synchronicity of the data introduces additional bias terms in the study of the estimator.