Advertisement

Big Data Analytics for High Frequency Trading Volatility Estimation

  • Henry Han
  • Maxwell Li
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

High frequency trading has been dominating finance industry recently. It brings big data and new problems in finance. How to estimate security volatility in high frequency trading remains a challenge in business analytics. In this study, we propose a novel section volatility estimation model and implement it via a big data analytics approach. The proposed method conquers the weakness of the conventional realized volatility by demonstrating the capability to capture both global and local behavior of volatility in the whole trading period besides robustness to the fine time intervals. To the best of our knowledge, this work is the first volatility study in high frequency trading by using big data analytics. It not only provides a fast and more accurate volatility estimation in high frequency trading, but also has its significance in finance theory and trading practice.

Keywords

Big data High frequency trading Volatility Spark 

References

  1. 1.
    Cespa, G., & Vives, X. (2017). High frequency trading and fragility (Working Papers Series). European Central Bank (2020).Google Scholar
  2. 2.
    Hendershott, et al. (2011). Does algorithmic trading improve liquidity? Journal of Finance, 66, 1–33.CrossRefGoogle Scholar
  3. 3.
    Brownlees, T., Cipollini, F., & Gallo, M. (2011). Intra-daily volume modelling and prediction for algorithmic trading. Journal of Financial Economics, 9(3), 489–518.CrossRefGoogle Scholar
  4. 4.
    Sirignano, J. (2017, June). Deep learning models in Finance. SIAM News.Google Scholar
  5. 5.
    Mitra, S. (2009). A review of volatility and option pricing: arxiv.org.
  6. 6.
    Spiegel, M. (1998). Stock price volatility in a multiple security overlapping generations model. Review of Financial Studies, 11(2), 419–447.CrossRefGoogle Scholar
  7. 7.
    Ait-Sahalia, et al. (2005). Ultra high frequency volatility estimation with dependent microstructure noise (BER Working Paper No. 11380).Google Scholar
  8. 8.
    Andersen, et al. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 61(1), 43–76.CrossRefGoogle Scholar
  9. 9.
    Zhang, et al. (2005). A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association, 100, 1391–1411.Google Scholar
  10. 10.
    Tsay, R., & Yeh, J. (2011). Random aggregation with applications in high-frequency finance. Journal of Forecast, 30, 72–103.CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Armbrust, et al. (2015). Spark SQL: Relational data processing in spark. Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 1383–1394).Google Scholar
  13. 13.
    Seddon, J., & Currie, W. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300–307.CrossRefGoogle Scholar
  14. 14.
    Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Laboratory of Big data and Analytics, Department of Computer and Information SciencesFordham UniversityNew YorkUSA
  2. 2.Business Analytics, Gabeli School of BusinessFordham UniversityNew YorkUSA

Personalised recommendations