Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data

Richard Y. Chen and Per A. Mykland

Journal of Econometrics, 2017, vol. 200, issue 1, 79-103

Abstract: In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Itô 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.

Keywords: Microstructure; High-frequency tests; Statistical powers; Stable central limit theorems; Non-stationarity; Volatility; Liquidity (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 C58 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:200:y:2017:i:1:p:79-103

DOI: 10.1016/j.jeconom.2017.05.015

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