Analysis of High Frequency Data in Finance: A Survey

George J. Jiang () and Guanzhong Pan ()
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George J. Jiang: Department of Finance and Management Science, College of Business,Washington State University, Pullman, WA 99164, USA
Guanzhong Pan: School of Finance, Yunnan University of Finance and Economics, Kunming 650221, China

Frontiers of Economics in China-Selected Publications from Chinese Universities, 2020, vol. 15, issue 2, 141-166

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.

Keywords: high frequency data; quadratic variation (QV); realized variance (RV); power variation (PV); bipower variation; jump tests; market microstructure noise; asset pricing (search for similar items in EconPapers)
JEL-codes: C01 C14 C58 G10 G12 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:fec:journl:v:15:y:2020:i:2:p:141-166

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