Big data analytics for financial Market volatility forecast based on support vector machine

Rongjun Yang, Lin Yu, Yuanjun Zhao, Hongxin Yu, Guiping Xu, Yiting Wu and Zhengkai Liu

International Journal of Information Management, 2020, vol. 50, issue C, 452-462

Abstract: High-frequency data provides a lot of materials and broad research prospects for in-depth research and understanding on financial market behavior, but the problems solved in the research of high-frequency data are far less than the problems faced and encountered, and the research value of high-frequency data will be greatly reduced without solving these problems. Volatility is an important measurement index of market risk, and the research and forecasting on the volatility of high-frequency data is of great significance to investors, government regulators and capital markets. To this end, by modelling the jump volatility of high-frequency data, the short-term volatility of high-frequency data are predicted.

Keywords: Big data; Financial market; Volatility; Support vector machine (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:50:y:2020:i:c:p:452-462

DOI: 10.1016/j.ijinfomgt.2019.05.027

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