Estimation of integrated covariances in the simultaneous presence of nonsynchronicity, microstructure noise and jumps

TL;DR

This paper introduces a novel high-frequency financial data estimator that effectively separates co-jumps from total covariation despite nonsynchronous sampling, noise, and jumps, improving empirical analysis accuracy.

Contribution

The paper presents the first estimator capable of simultaneously addressing nonsynchronicity, microstructure noise, and jumps in high-frequency data analysis.

Findings

Estimator demonstrates asymptotic mixed normality under mild conditions.

Monte Carlo simulations confirm good finite sample performance.

Handles infinite activity jumps and endogenous observation errors.

Abstract

We propose a new estimator for the integrated covariance of two Ito semimartingales observed at a high-frequency. This new estimator, which we call the pre-averaged truncated Hayashi-Yoshida estimator, enables us to separate the sum of the co-jumps from the total quadratic covariation even in the case that the sampling schemes of two processes are nonsynchronous and the observation data is polluted by some noise. It is the first estimator which can simultaneously handle these three issues, which are fundamental to empirical studies of high-frequency financial data. We also show the asymptotic mixed normality of this estimator under some mild conditions allowing infinite activity jump processes with finite variations, some dependency between the sampling times and the observed processes as well as a kind of endogenous observation errors. We examine the finite sample performance of this estimator using a Monte Carlo study.