Efficient covariance estimation for asynchronous noisy high-frequency data

TL;DR

This paper introduces an efficient, unbiased estimator for integrated covariance of high-frequency asynchronous noisy data, combining synchronization and multi-scale techniques to achieve optimal convergence rates.

Contribution

The paper presents a novel estimator that effectively handles asynchronous and noisy high-frequency data, improving accuracy and efficiency over existing methods.

Findings

Estimator attains the optimal rate of convergence.

Monte Carlo simulations demonstrate good finite sample performance.

Method effectively combines synchronization and multi-scale approaches.

Abstract

We focus on estimating the integrated covariance of log-price processes in the presence of market microstructure noise. We construct an efficient unbiased estimator for the quadratic covariation of two It\^{o} processes in the case where high-frequency asynchronous discrete returns under market microstructure noise are observed. This estimator is based on synchronization and multi-scale methods and attains the optimal rate of convergence. A Monte Carlo study analyzes the finite sample size characteristics of our estimator.