Model-Free Approaches to Discern Non-Stationary Microstructure Noise and Time-Varying Liquidity in High-Frequency Data

TL;DR

This paper develops non-parametric statistical tools to test for non-stationarity in microstructure noise and measure liquidity risk in high-frequency financial data, with theoretical validation and empirical application to NYSE data.

Contribution

It introduces three novel tests for microstructure noise stationarity and liquidity risk measurement, with proven asymptotic properties and practical empirical validation.

Findings

Non-stationary microstructure noise is prevalent in NYSE data.

The proposed tests effectively detect non-stationarity with controlled error rates.

Simulation results support the theoretical properties of the tests.

Abstract

In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Ito semimartingales, and discuss how to measure liquidity risk using high frequency financial data. In particular, we investigate the impact of non-stationary microstructure noise on some volatility estimators, and design three complementary tests by exploiting edge effects, information aggregation of local estimates and high-frequency asymptotic approximation. The asymptotic distributions of these tests are available under both stationary and non-stationary assumptions, thereby enable us to conservatively control type-I errors and meanwhile ensure the proposed tests enjoy the asymptotically optimal statistical power. Besides it also enables us to empirically measure aggregate liquidity risks by these test statistics. As byproducts, functional dependence and endogenous microstructure noise are briefly discussed. Simulation with a realistic configuration corroborates our theoretical results, and our empirical study indicates the prevalence of non-stationary microstructure noise in New York Stock Exchange.