George J. Jiang a, Guanzhong Pan b
Author information
a Department of Finance and Management Science, College of Business,Washington State University, Pullman, WA 99164, USA
b School of Finance, Yunnan University of Finance and Economics, Kunming 650221, China
E-mail: george.jiang@wsu.edu (George J. Jiang), panguanzhong@126.com (Guanzhong Pan)
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.
high frequency data, quadratic variation (QV), realized variance (RV), power variation (PV), bipower variation, jump tests, market microstructure noise, asset pricing
George J. Jiang, Guanzhong Pan. Analysis of High Frequency Data in Finance: A Survey. Front. Econ. China, 2020, 15(2): 141‒166 https://doi.org/10.3868/s060-011-020-0007-1
