Robust estimation and inference for jumps in noisy high frequency data: A Local-to-Continuity Theory for the pre-averaging method

• Main Author: LI, Jia
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Confidence set; high frequency data; jump power variation; market microstructure noise; pre-averaging; semimartingale; unformity; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2580; https://ink.library.smu.edu.sg/context/soe_research/article/3579/viewcontent/Econometrica___2013___Li___Robust_Estimation_and_Inference_for_Jumps_in_Noisy_High_Frequency_Data__A_Local_to_Continuity.pdf
• Institution: Singapore Management University

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.