Realised quantile-based estimation of the integrated variance

TL;DR

This paper introduces a new quantile-based realized variance estimator that is robust to jumps, outliers, and market microstructure noise, providing consistent and efficient measurement of integrated variance from noisy high-frequency data.

Contribution

It proposes a novel jump-robust quantile-based estimator for integrated variance that is consistent, efficient, and applicable to high-frequency noisy data.

Findings

Estimator is consistent and efficient for integrated variance.

Robust to jumps and outliers in price series.

Effective with market microstructure noise in high-frequency data.

Abstract

In this paper, we propose a new jump robust quantile-based realised variance measure of ex-post return variation that can be computed using potentially noisy data. The estimator is consistent for the integrated variance and we present feasible central limit theorems which show that it converges at the best attainable rate and has excellent efficiency. Asymptotically, the quantile-based realised variance is immune to finite activity jumps and outliers in the price series, while in modified form the estimator is applicable with market microstructure noise and therefore operational on high-frequency data. Simulations show that it has superior robustness properties in finite sample, while an empirical application illustrates its use on equity data.