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

Author

Listed:
  • Yang, Rongjun
  • Yu, Lin
  • Zhao, Yuanjun
  • Yu, Hongxin
  • Xu, Guiping
  • Wu, Yiting
  • Liu, Zhengkai

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.

Suggested Citation

  • Yang, Rongjun & Yu, Lin & Zhao, Yuanjun & Yu, Hongxin & Xu, Guiping & Wu, Yiting & Liu, Zhengkai, 2020. "Big data analytics for financial Market volatility forecast based on support vector machine," International Journal of Information Management, Elsevier, vol. 50(C), pages 452-462.
    • Handle: RePEc:eee:ininma:v:50:y:2020:i:c:p:452-462
    DOI: 10.1016/j.ijinfomgt.2019.05.027
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    Citations

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    Cited by:

    1. Asima Saleem, 2022. "Action for Action: Mad COVID-19, Falling Markets and Rising Volatility of SAARC Region," Annals of Data Science, Springer, vol. 9(1), pages 33-54, February.
    2. Huang, Chuangxia & Zhao, Xian & Deng, Yunke & Yang, Xiaoguang & Yang, Xin, 2022. "Evaluating influential nodes for the Chinese energy stocks based on jump volatility spillover network," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 81-94.
    3. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    4. Dhruhi Sheth & Manan Shah, 2023. "Predicting stock market using machine learning: best and accurate way to know future stock prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 1-18, February.
    5. Ayan Chatterjee & Debmallya Chatterjee, 2024. "A Journey of Business Analytics in Improving Supply Chain Performance: A Systematic Review of Literature," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 49(2), pages 337-361, May.
    6. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    7. Henao-Londono, Juan C. & Guhr, Thomas, 2022. "Foreign exchange markets: Price response and spread impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    8. Zhigui Guan & Yuanjun Zhao & Guojing Geng, 2022. "The Risk Early-Warning Model of Financial Operation in Family Farms Based on Back Propagation Neural Network Methods," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1221-1244, December.
    9. Jacqueline Birt & Maryam Safari & Vincent Bicudo de Castro, 2023. "Critical analysis of integration of ICT and data analytics into the accounting curriculum: A multidimensional perspective," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4037-4063, December.

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