IMC Colloquium Series: "Jump Detection with Wavelets for High-Frequency Financial Time Series"

Abstract

This paper introduces a new nonparametric test based on wavelets to detect jump arrival times in high frequency financial time series data. The asymptotic distribution of the test is derived. We demonstrate that the test is robust for different specifications of price processes and the presence of the microstructure noise. A Monte Carlo simulation is conducted to show that the test has good power and size. Further, we examine the multi-scale jump dynamics in U.S. equity markets. The main findings are as follows. First, the jump dynamics of equities are entirely different across different time scales, suggesting that choosing a proper sampling frequency is important to extract full jump dynamics. Second, although arrival densities of positive jumps and negative jumps are symmetric across different time scales, the magnitude of jumps is distributed asymmetrically at high frequencies. Third, only twenty percent of jumps occur in the trading session from 9:30AM to 4:00PM, suggesting that jumps are largely determined by news rather than liquidity shocks.