Paper Info
Title
Big data analytics for financial Market volatility forecast based on support vector machine
Abstract
•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.
Year
DOI
Venue
2020
10.1016/j.ijinfomgt.2019.05.027
International Journal of Information Management
Keywords
Field
DocType
Big data,Financial market,Volatility,Support vector machine
Financial economics,Capital market,Market risk,Support vector machine,Engineering,Financial market,Jump,Big data,Volatility (finance),Marketing,Government
Journal
Volume
ISSN
Citations 
50
0268-4012
2
PageRank 
References 
Authors
0.36
0
7
Name
Order
Citations
PageRank
Rongjun Yang120.70
Lin Yu220.36
Yuanjun Zhao3146.42
Hongxin Yu420.36
Guiping Xu520.36
Yiting Wu620.36
Zhengkai Liu750.78