Big Data Analytics for High Frequency Trading Volatility Estimation
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.
KeywordsBig data High frequency trading Volatility Spark
- 1.Cespa, G., & Vives, X. (2017). High frequency trading and fragility (Working Papers Series). European Central Bank (2020).Google Scholar
- 4.Sirignano, J. (2017, June). Deep learning models in Finance. SIAM News.Google Scholar
- 5.Mitra, S. (2009). A review of volatility and option pricing: arxiv.org.
- 7.Ait-Sahalia, et al. (2005). Ultra high frequency volatility estimation with dependent microstructure noise (BER Working Paper No. 11380).Google Scholar
- 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
- 11.Spark. (2017). https://spark.apache.org/
- 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