Big data analytics for financial Market volatility forecast based on support vector machine. (February 2020)

• Record Type: Journal Article
• Authors: Yang, Rongjun; Yu, Lin; Zhao, Yuanjun; Yu, Hongxin; Xu, Guiping; Wu, Yiting; Liu, Zhengkai
• Is Part Of: International journal of information management. Volume 50(2020)
• Page Start: 452
• Page End: 462
• Publication Date: 2020-02
• Subjects: Big data -- Financial market -- Volatility -- Support vector machine; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2019.05.027
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: 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. The realized volatility and the realized bi-power variation have the obvious phenomenon of fluctuation aggregation, and have the auto-correlation; different from the auto-correlation of logarithmic yield, the auto-correlation of the realized volatility and the realized bi-power variation is relatively strong, and the correlation is positive. The verification results of verification data obtained by fitting HAR-RV model, HAR-lnRV model and HAR-JV-CV model show that: the HAR-lnRV model has the best prediction effect, followed by the HAR-JV-CV model, and the worst is the HAR-RV model. 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.