Volatility Inference in the Presence of Both Endogenous Time and Microstructure Noise

TL;DR

This paper develops and analyzes new estimators for integrated volatility that account for both market microstructure noise and endogenous sampling times, demonstrating improved performance over existing methods.

Contribution

It introduces novel volatility estimators that handle endogenous time and microstructure noise, with theoretical analysis and empirical validation.

Findings

Proposed estimators outperform existing methods under endogenous time conditions.

Theoretical properties of the estimators are established.

Numerical studies confirm improved accuracy in practical scenarios.

Abstract

In this article we consider the volatility inference in the presence of both market microstructure noise and endogenous time. Estimators of the integrated volatility in such a setting are proposed, and their asymptotic properties are studied. Our proposed estimator is compared with the existing popular volatility estimators via numerical studies. The results show that our estimator can have substantially better performance when time endogeneity exists.