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摘要翻译：
在本文中，我们对在给定时间跨度内波动率过程是否为常数进行了研究，并利用高频数据对波动率过程进行了测试，同时考虑了波动率的跳跃和微结构噪声。在综合波动率和现货波动率估计的基础上，我们提出了一种非参数方法来描述局部波动率和全局波动率之间的差异。我们证明了当波动率为常数时，我们提出的检验估计收敛于标准正态分布，否则它发散到无穷大。仿真研究验证了理论结果，并表明该试验程序具有良好的有限样本性能。我们还应用我们的检验程序对一些真实的高频金融数据进行了异方差检验。我们观察到，在测试的几乎一半的日子里，一天内波动不变的假设被违反了。这是由于股票开盘和收盘时的价格波动性很大，在盘中波动中所占的比例相对较大。
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英文标题：
《Heteroscedasticity test of high-frequency data with jumps and
microstructure noise》
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作者：
Qiang Liu and Zhi Liu and Chuanhai Zhang
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/2010.07659
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