A Consistent and Robust Test for Autocorrelated Jump Occurrences

Abstract

We develop a nonparametric test for the temporal dependence of jump occurrences in the population. The test is consistent against all pairwise serial dependence, and is robust to the jump activity level and the choice of sampling scheme. We establish asymptotic normality and local power property for a rich set of local alternatives, including both self-exciting and/or self-inhibitory jumps. Simulation study confirms the robustness of the test and reveals its competitive size and power performance over existing tests. In an empirical study on high-frequency stock returns, our procedure uncovers a wide array of autocorrelation profiles of jump occurrences for different stocks in different time periods.