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

Prof. Yacine Aït-Sahalia

Princeton University

About the Speaker

Yacine Aït-Sahalia is the Otto Hack 1903 Professor of Finance and Economics at Princeton University. His primary area of research is financial econometrics. Prior to that, he was an Assistant Professor (1993–1996), Associate Professor (1996–1998) and Professor of Finance (1998) at the University of Chicago Booth School of Business.

He received his undergraduate degree from École Polytechnique in Paris, France in 1987 and from École Nationale des Statistiques et de l’Administration Economique in 1989, and his Ph.D. in Economics from the Massachusetts Institute of Technology in 1993.

He has served as Editor of the Review of Financial Studies (2003–2006), Co-Editor of the Journal of Econometrics (since 2012), and Associate Editor of the Annals of Statistics (2003–2006), Econometrica (2007–2013), the Journal of Finance (2007–2010), Finance and Stochastics (1996–2011), the Journal of Econometrics (1999–2012) and the Journal of Financial Econometrics (2001–2011). He served as Director of the Western Finance Association (2003–2006) and is a Research Associate for the National Bureau of Economic Research (since 1995).

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.

This is a joint work with Dacheng Xiu.