Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News
University of Geneva - Graduate School of Business (HEC-Geneva)
University of Geneva - HEC; Swiss Finance Institute
University of Geneva; Swiss Finance Institute
July 26, 2013
Swiss Finance Institute Research Paper No. 11-36
Applying tests for jumps to financial data sets can lead to an important number of spurious detections. Bursts of volatility are often incorrectly identified as jumps when the sampling is too sparse. At a higher frequency, methods robust to microstructure noise are required. We argue^that whatever the jump detection test and the sampling frequency, a highly elevant number of spurious detections remain because of multiple testing issues. We propose a formal treatment based on an explicit thresholding on available test statistics. We prove that our method eliminates asymptotically all remaining spurious detections. In Dow Jones stocks between 2006 and 2008, spurious detections can represent up to 90% of the jumps detected initially. For the stocks considered, jumps are rare events, they do not cluster in time, and no cojump aects all stocks simultaneously, suggesting jump risk is diversiable. We relate the remaining jumps to macroeconomic news, prescheduled company-specic announcements, and stories from news agencies which include a variety of unscheduled and uncategorized events. The vast majority of news do not cause jumps but may generate a market reaction of the form of bursts of volatility.
Number of Pages in PDF File: 43
Keywords: jumps, high-frequency data, spurious detections, jumps dynamics, news releases, cojumps
JEL Classification: C58, G12, G14working papers series
Date posted: February 15, 2009 ; Last revised: July 26, 2013
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