Abstract
This paper proposes a new jump test for semi-martingale contained by microstructure noise based on the threshold pre-averaging bi-power estimation. Theoretically, we prove that such test has asymptotical size and power. Monte Carlo simulations show that the new test has better performance than Christensen et al(2014)'s test in noisy setting and we also consider adopting the false discovery rate (FDR) threshold technique to avoid spurious detections. In the empirical part, we investigate the contributions of jumps to total return variance from the Chinese stock market based on the tick-by-tick transaction data. The empirical results imply that the jump variation is an order of magnitude smaller than typical estimates found in the existing literature from different perspectives.
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