On the estimation of integrated covariance matrices of high dimensional diffusion processes

TL;DR

This paper investigates the spectral properties of high-frequency estimators of integrated covariance matrices in high-dimensional diffusion processes, revealing how time variability affects their distributions and proposing an improved estimator.

Contribution

It introduces a Marčenko–Pastur type theorem for weighted covariance matrices and proposes the TVARCV estimator that accurately reflects the ICV's spectral distribution.

Findings

The LSD of RCV depends on covolatility variation over time.

The TVARCV estimator's LSD depends only on the ICV's LSD.

TVARCV can recover the spectral distribution of the ICV.

Abstract

We consider the estimation of integrated covariance (ICV) matrices of high dimensional diffusion processes based on high frequency observations. We start by studying the most commonly used estimator, the realized covariance (RCV) matrix. We show that in the high dimensional case when the dimension $p$ and the observation frequency $n$ grow in the same rate, the limiting spectral distribution (LSD) of RCV depends on the covolatility process not only through the targeting ICV, but also on how the covolatility process varies in time. We establish a Mar\v{c}enko--Pastur type theorem for weighted sample covariance matrices, based on which we obtain a Mar\v{c}enko--Pastur type theorem for RCV for a class $\mathcal{C}$ of diffusion processes. The results explicitly demonstrate how the time variability of the covolatility process affects the LSD of RCV. We further propose an alternative estimator, the time-variation adjusted realized covariance (TVARCV) matrix. We show that for processes in class $\mathcal {C}$, the TVARCV possesses the desirable property that its LSD depends solely on that of the targeting ICV through the Mar\v{c}enko--Pastur equation, and hence, in particular, the TVARCV can be used to recover the empirical spectral distribution of the ICV by using existing algorithms.