Jumps in high-frequency data : spurious detections, dynamics, and news

We propose a technique to avoid spurious detections of jumps in high-frequency data via an explicit thresholding on available test statistics. We prove that it eliminates asymptotically all spurious detections. Monte Carlo results show that it performs also well in finite samples. In Dow Jones stocks, spurious detections represent up to 50% of the jumps detected initially between 2006 and 2008. For the majority of stocks, jumps do not cluster in time and no cojump affects all stocks simultaneously, suggesting jump risk is diversifiable. We relate the remaining jumps to macroeconomic news, prescheduled company-specific announcements, and stories from news agencies which include a variety of unscheduled and uncategarized events. The majority of news do not cause jumps. One exception are share buybacks announcements. Fed rate news have an important impact but rarely cause jumps. Another finding is that 60% of jumps occur without any news event. For one third of the jumps with no news we observe an unusual behavior in the volume of transactions. Hence, liquidity pressures are probably another important factor of jumps. Jumps, High-Frequency Data, Spurious Detections, Jumps Dynamics, News Releases, Cojumps