Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2013
Abstract
We develop an asymptotic theory for the pre-averaging estimator when asset price jumps are weakly identified, here modeled as local to zero. The theory unifies the conventional asymptotic theory for continuous and discontinuous semimartingales as two polar cases with a continuum of local asymptotics, and explains the breakdown of the conventional procedures under weak identification. We propose simple bias-corrected estimators for jump power variations, and construct robust confidence sets with valid asymptotic size in a uniform sense. The method is also robust to certain forms of microstructure noise.
Keywords
Confidence set, high frequency data, jump power variation, market microstructure noise, pre-averaging, semimartingale, unformity
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometrica
Volume
81
Issue
3
First Page
1673
Last Page
1693
ISSN
0012-9682
Identifier
10.3982/ECTA10534
Publisher
Econometric Society
Citation
LI, Jia.
Robust estimation and inference for jumps in noisy high frequency data: A Local-to-Continuity Theory for the pre-averaging method. (2013). Econometrica. 81, (3), 1673-1693.
Available at: https://ink.library.smu.edu.sg/soe_research/2580
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3982/ECTA10534