论文标题
具有跳跃和微观结构噪声的高频数据的异质性测试
Heteroscedasticity test of high-frequency data with jumps and microstructure noise
论文作者
论文摘要
在本文中，我们有兴趣测试在给定时间范围内通过使用跳跃和微观结构噪声的高频数据在给定时间跨度期间的波动过程。根据综合波动率和斑点波动率的估计量，我们提出了一种非参数方式来描述局部变异和全球变化之间的差异。我们表明，如果波动率恒定，我们提出的测试估计器会收敛到标准正态分布，否则它会分歧为无穷大。仿真研究验证了理论结果，并显示了测试程序的良好样本性能。我们还应用测试程序来进行异质性测试，以获取一些实际的高频财务数据。我们观察到，在测试的几乎一半的日子里，一天内持续波动率的假设被违反。这是由于开放期间和收盘期间的股票价格高度波动，并且在日内变化中相对相对较大。
In this paper, we are interested in testing if the volatility process is constant or not during a given time span by using high-frequency data with the presence of jumps and microstructure noise. Based on estimators of integrated volatility and spot volatility, we propose a nonparametric way to depict the discrepancy between local variation and global variation. We show that our proposed test estimator converges to a standard normal distribution if the volatility is constant, otherwise it diverges to infinity. Simulation studies verify the theoretical results and show a good finite sample performance of the test procedure. We also apply our test procedure to do the heteroscedasticity test for some real high-frequency financial data. We observe that in almost half of the days tested, the assumption of constant volatility within a day is violated. And this is due to that the stock prices during opening and closing periods are highly volatile and account for a relative large proportion of intraday variation.
