Pattern Recognition of Stock Returns in the Very Short Run Leveraging High-Frequency Financial Data

Research Article
Open access

Pattern Recognition of Stock Returns in the Very Short Run Leveraging High-Frequency Financial Data

Yan Gao 1*
  • 1 Ocean University of China    
  • *corresponding author rgao0829@gmail.com
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/19/20230159
AEMPS Vol.19
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-81-2
ISBN (Online): 978-1-915371-82-9

Abstract

This paper uses high frequency stock trading data from 2007 to 2014 to study patterns of stock returns in the very short run. The paper verifies stylized facts of stock prices at low frequency with the exploration of high-frequency data and uses the concept of relative realized volatility (RRV) to measure volatility to understand the market uncertainty intraday. By providing a large number of empirical data facts, this paper advocates the use of ultra-high frequency data to study instantaneous real volatility, and demonstrates that long-term market volatility and the relationship between short and long term volatility can be implied by a simple RRV mean regression model.

Keywords:

volatility, high-frequency, stock price, market crash

Gao,Y. (2023). Pattern Recognition of Stock Returns in the Very Short Run Leveraging High-Frequency Financial Data. Advances in Economics, Management and Political Sciences,19,353-365.
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References

[1]. Zhang, F. (2010). The effect of high-frequency trading on stock volatility and price discovery. SSRN eLibrary.

[2]. Zhang, L. (2006) Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach", Bernoulli, 12, 1019-1043.

[3]. Heston, S. (1993). A closed-form solution for options with stochastic volatility with applications to bonds and currency options. Review of Financial Studies, 6(2), 327–343.

[4]. Guilbaud, F., & Pham, H. (2013). Optimal high-frequency trading with limit and market orders. Quantitative Finance, 13(1), 79-94.

[5]. Engle, R., Ghysels, E., & Sohn, B. (2008). On the economic sources of stock market volatility. SSRN Electronic Journal.

[6]. Bollerslev, T., & Zhou, H. (2002). Estimating stochastic volatility diffusion using conditional moments of integrated volatility. Journal of Econometrics, 109(1), 33–65.

[7]. Sun, M. (2016). Modeling volatility using high-frequency data (Unpublished doctoral dissertation). UCLA, Los Angeles, CA.

[8]. Ellickson, B., Sun, M., Whang, D., & Yan, S. (2018). Estimating a local Heston model. SSRN Archive.

[9]. Schwert, G.W. (1989). Why does stock market volatility change over time? The Journal of Finance, 44(5), 1115–1153.

[10]. Blitz, D., & van Vliet, P. (2007). The volatility effect: Lower risk without lower return. Journal of Portfolio Management, 102-113.


Cite this article

Gao,Y. (2023). Pattern Recognition of Stock Returns in the Very Short Run Leveraging High-Frequency Financial Data. Advances in Economics, Management and Political Sciences,19,353-365.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Management Research and Economic Development

ISBN:978-1-915371-81-2(Print) / 978-1-915371-82-9(Online)
Editor:Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://2023.icmred.org/
Conference date: 28 April 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.19
ISSN:2754-1169(Print) / 2754-1177(Online)

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References

[1]. Zhang, F. (2010). The effect of high-frequency trading on stock volatility and price discovery. SSRN eLibrary.

[2]. Zhang, L. (2006) Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach", Bernoulli, 12, 1019-1043.

[3]. Heston, S. (1993). A closed-form solution for options with stochastic volatility with applications to bonds and currency options. Review of Financial Studies, 6(2), 327–343.

[4]. Guilbaud, F., & Pham, H. (2013). Optimal high-frequency trading with limit and market orders. Quantitative Finance, 13(1), 79-94.

[5]. Engle, R., Ghysels, E., & Sohn, B. (2008). On the economic sources of stock market volatility. SSRN Electronic Journal.

[6]. Bollerslev, T., & Zhou, H. (2002). Estimating stochastic volatility diffusion using conditional moments of integrated volatility. Journal of Econometrics, 109(1), 33–65.

[7]. Sun, M. (2016). Modeling volatility using high-frequency data (Unpublished doctoral dissertation). UCLA, Los Angeles, CA.

[8]. Ellickson, B., Sun, M., Whang, D., & Yan, S. (2018). Estimating a local Heston model. SSRN Archive.

[9]. Schwert, G.W. (1989). Why does stock market volatility change over time? The Journal of Finance, 44(5), 1115–1153.

[10]. Blitz, D., & van Vliet, P. (2007). The volatility effect: Lower risk without lower return. Journal of Portfolio Management, 102-113.