A Hausman Test for the Presence of Market Microstructure Noise in High Frequency Data

55 Pages Posted: 7 Mar 2016 Last revised: 21 Feb 2017

See all articles by Yacine Ait-Sahalia

Yacine Ait-Sahalia

Princeton University - Department of Economics; National Bureau of Economic Research (NBER)

Dacheng Xiu

University of Chicago - Booth School of Business

Date Written: February 17, 2017

Abstract

We develop tests that help assess whether a high frequency data sample can be treated as reasonably free of market microstructure noise at a given sampling frequency for the purpose of implementing high frequency volatility and other estimators. The tests are based on the Hausman principle of comparing two estimators, one that is efficient but not robust to the deviation being tested, and one that is robust but not as efficient. We investigate the asymptotic properties of the test statistic in a general nonparametric setting, and compare it with several alternatives that are also developed in the paper. Empirically, we find that improvements in stock market liquidity over the past decade have increased the frequency at which simple, uncorrected, volatility estimators can be safely employed.

Keywords: Hausman test, market microstructure noise, realized volatility, QMLE, TSRV, preaveraging, super-efficiency, local power

JEL Classification: C13, C14, C55, C58, G01

Suggested Citation

Ait-Sahalia, Yacine and Xiu, Dacheng, A Hausman Test for the Presence of Market Microstructure Noise in High Frequency Data (February 17, 2017). Chicago Booth Research Paper No. 16-06. Available at SSRN: https://ssrn.com/abstract=2741911 or http://dx.doi.org/10.2139/ssrn.2741911

Yacine Ait-Sahalia

Princeton University - Department of Economics ( email )

Fisher Hall
Princeton, NJ 08544
United States
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National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
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Dacheng Xiu (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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