Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book

Simon Clinet and Yoann Potiron

Journal of Econometrics, 2019, vol. 209, issue 2, 289-337

Abstract: In this paper, we build tests for the presence of residual noise in a model where the market microstructure noise is a known parametric function of some variables from the limit order book. The tests compare two distinct quasi-maximum likelihood estimators of volatility, where the related model includes a residual noise in the market microstructure noise or not. The limit theory is investigated in a general nonparametric framework. In the presence of residual noise, we examine the central limit theory of the related quasi-maximum likelihood estimation approach.

Keywords: Efficient price; Estimation; High frequency data; Information; Limit order book; Market microstructure noise; Integrated volatility; Quasi-maximum likelihood estimator; Realized volatility; Test (search for similar items in EconPapers)
JEL-codes: C01 C02 C13 C14 C22 C58 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407619300089
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:209:y:2019:i:2:p:289-337

DOI: 10.1016/j.jeconom.2019.01.004

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().