A0350
Title: Separate noise and jumps from tick data: An endogenous thresholding approach
Authors: Xiaolu Zhao - Dongbei University of Finance and Economics (China)
Oliver Linton - University of Cambridge (United Kingdom)
Seok Young Hong - Lancaster University Management School (United Kingdom) [presenting]
Abstract: The problem of jump detection for ultra-high-frequency tick-by-tick data is studied. We propose a novel easy-to-implement procedure that can separate the contribution of microstructure noise and finite activity price jumps from the price process, which may have interesting implications on asset pricing and forecasting problems. We provide theoretical grounds for our approach and suggests practical guidelines for determining the tuning parameter. Making a comparison with the star performers in a recent comprehensive review for jump detection methods as well as a test on tick data, we show that the method performs admirably well via extensive simulations and rich empirical illustrations.
