Measuring the Jump Risk Contribution under Market Microstructure Noise – Evidence from Chinese Stock Market

In this paper, we use the pre-averaging threshold method to measure the contribution of jump variation to the total price variation under the effect of market microstructure noise with financial high frequency data. We first show the advantages of our method by Monte Carlo simulation. Then, we apply the pre-averaging threshold estimator and bi-power variation estimator for comparison to the intraday data of Chinese stock market at different frequencies. The empirical results show that for the most stocks in our sample, the jump contribution estimated by noise-robust estimator at tick frequency is larger than the result at five-minute frequency, which is different from the result for US market that the jump variation is overestimated with lower-frequency data in Christensen et al. (2014). Moreover, jump jump component is an important contributor to the total risk in Chinese stock market.