Analysis of High Frequency Data in Finance: A Survey
2 作者信息
George J. Jiang
Washington State University
Guanzhong Pan
Yunnan University of Finance and Economics
3 出处
George J. Jiang, Guanzhong Pan. Analysis of High Frequency Data in Finance: A Survey[J]. Front. Econ. China, 2020, 15(2): 141-166.
链接
http://journal.hep.com.cn/fec/EN/10.3868/s060-011-020-0007-1
http://journal.hep.com.cn/fec/EN/Y2020/V15/I2/141
4 摘要
Abstract: This study examines the use of high frequency data in finance, including volatility estimation and jump tests. High frequency data allows the construction of model-free volatility measures for asset returns. Realized variance is a consistent estimator of quadratic variation under mild regularity conditions. Other variation concepts, such as power variation and bipower variation, are useful and important for analyzing high frequency data when jumps are present. High frequency data can also be used to test jumps in asset prices. We discuss three jump tests: bipower variation test, power variation test, and variance swap test in this study. The presence of market microstructure noise complicates the analysis of high frequency data. The survey introduces several robust methods of volatility estimation and jump tests in the presence of market microstructure noise. Finally, some applications of jump tests in asset pricing are discussed in this article.
姜近勇
华盛顿州立大学
潘冠中
云南财经大学
摘要:本文考察波动率估计和跳跃检验两类高频数据在金融领域的应用。高频数据允许构建不依赖模型的资产收益波动度量。在弱正则性条件下,已实现方差是二次变差的一致估计量。其他变差概念,如幂变差和双幂变差,对于分析存在跳跃情况下的高频数据不仅有用而且重要。高频数据还可以用来检验资产价格的跳跃。本文探讨了三种跳跃检验:双幂变差检验、幂变差检验和方差互换检验。市场微观结构噪音的存在使得高频数据分析更为复杂。因此我们介绍了几种稳健的波动率估计和跳跃检验方法。最后,本文还讨论了跳跃检验在资产定价中的一些应用。
作者简介:
姜近勇: 华盛顿州立大学卡森商学院管理科学与金融学系Brinson投资管理讲座教授, 研究领域涉及资本市场有效性、实证资产定价、利率模型、风险管理、波动率估计、期权定价、共同基金业绩评估等。曾在Journal of Financial Economics,Review of Financial Studies, Journal of Financial and Quantitative Analysis, Journal of Econometrics 等金融学和计量经济学国际期刊发表高水平学术论文50余篇,出版中文教材《金融计量学》。现兼任学术期刊Journalof Financial Research副主编。
潘冠中:云南财经大学金融学院教授,硕士研究生导师。研究方向为实证资产定价,风险管理和期权定价,和姜近勇教授合作出版中文教材《金融计量学》。