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.

中文翻译：

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基于高频数据测试波动率跳跃

本文基于高频数据测试波动率跳跃。在波动过程是连续半鞅的原假设下，我们的检验统计量收敛到正态分布，在波动率有跳跃的备择假设下，统计量发散到无穷大。与 Bibinger等人的测试统计量相比。（Bibinger et al. (2017). Annals of Statistics 45, 1542–1578），我们提出的统计以更快的速度发散到无穷大，并且具有更好的功效。仿真研究证实了理论结果，实证分析表明，一些真实的金融数据具有波动性跳跃。