Testing the volatility jumps based on the high frequency data
本文提出一种基于高频数据的波动率跳跃检验方法，统计量在波动率连续时收敛到正态分布，存在跳跃时发散，且发散速度优于现有方法，模拟和实证验证了有效性。
This article tests volatility jumps based on the high frequency data. Under the null hypothesis that the volatility process is a continuous semimartingale, our test statistic converges to a normal distribution, and under the alternative hypothesis where the volatility has jumps, the statistic diverges to infinity. Compared to the test statistic of Bibinger et al. (Bibinger et al. (2017). Annals of Statistics 45, 1542–1578), our proposed statistic diverges to infinity at a faster rate, and has a better power. Simulation studies confirm the theoretical results, and an empirical analysis shows that some real financial data possess volatility jumps.