Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data
Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data
复制标题
识别高频数据中非平稳微观结构噪声和时变流动性的无模型方法
DOI：
10.1016/j.jeconom.2017.05.015
复制
发表时间：
2017
影响因子：
6.3
通讯作者：
Mykland, Per A.
中科院分区：
文献类型：
--
作者：
Chen, Richard Y.;Mykland, Per A.
本文提供了非参数统计工具来检验一般隐含的微观结构噪声的平稳性，并讨论了如何利用高频金融数据来度量流动性风险。特别地，我们考察了非平稳微结构噪声对一些波动率估计量的影响，并利用边缘效应、局部估计的信息聚合和高频渐近逼近设计了三个互补检验。这些检验的渐近分布可以在平稳和非平稳假设下得到，从而使我们能够保守地控制第I类误差，同时确保所提出的检验具有渐近最优的统计能力。此外，它还使我们能够通过这些测试统计数据来实证衡量总流动性风险。作为副产品，简要讨论了功能依赖和内源微结构噪声。真实构型的模拟验证了我们的理论结果，我们的实证研究表明，纽约证券交易所普遍存在非平稳微结构噪声。
In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Itô semimartingales, and discuss how to measure liquidity risk using high-frequency financial data. In particular, we investigate the impact of non-stationary microstructure noise on some volatility estimators, and design three complementary tests by exploiting edge effects, information aggregation of local estimates and high-frequency asymptotic approximation. The asymptotic distributions of these tests are available under both stationary and non-stationary assumptions, thereby enable us to conservatively control type-I errors and meanwhile ensure the proposed tests enjoy the asymptotically optimal statistical power. Besides, it also enables us to empirically measure aggregate liquidity risks by these test statistics. As byproducts, functional dependence and endogenous microstructure noise are briefly discussed. Simulation with a realistic configuration corroborates our theoretical results, and our empirical study indicates the prevalence of non-stationary microstructure noise in New York Stock Exchange.