Jumps at Ultra-High Frequency: Evidence From the Chinese Stock Market

2019 ◽  
Author(s):  
Chuanhai Zhang ◽  
Zhi Liu ◽  
Qiang Liu
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Chinese Stock Market ◽  
Ultra High Frequency

scholarly journals The Price Impact of Order Book Events from a Dimension of Time

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wentao Chi ◽  
Xuemei Zhao ◽  
Lufei Huang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Time Window ◽  
Explanatory Power ◽  
Chinese Mainland ◽  
Price Impact ◽  
Order Book ◽  
Chinese Stock Market ◽  
Time Dimension ◽  
The Difference

We propose a new linear model to explain the price move by Level-2 high-frequency data in Chinese mainland stock market. In Chinese stock market, the cancellation ratio is very low, and imbalanced order flow prevails most of the time in the trading periods. From time dimension viewpoint, we find the difference of efficiency of limit orders executed, respectively, in bid/ask limit order book, order execution imbalance (OEI), could improve the classic model of Cont et al. (2014) based on market microstructure of Chinese mainland stock market. In particular, when market’s liquidity is booming, our model’s explanatory power and R-squared increased sharply. And the correlations of OEI are very high that may be exploited to predict the price move in the next time window for doing high-frequency trading.

scholarly journals Modeling Financial Intraday Jump Tail Contagion with High Frequency Data Using Mutually Exciting Hawkes Process

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chao Yu ◽  
Jianxin Bi ◽  
Xujie Zhao
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
High Frequency ◽  
Asset Price ◽  
High Frequency Data ◽  
Background Intensity ◽  
Chinese Stock Market ◽  
Hawkes Process ◽  
Frequency Data ◽  
The Stability

Financial extreme jumps in asset price may propagate across stock markets and lead to the market-wide crashes, which severely threatens the stability of the financial system. In order to analyzing the contagion features of jump tail risk, this paper proposes a mutually exciting contagion model based on Hawkes process with intraday high frequency data. We use a simple two-stage method that first extracts the jump component nonparametrically from the high frequency data and then models the intraday jump tail using mutually exciting Hawkes process. Moreover, we take both the occurrence time and magnitude of jump into account in modeling the conditional intensity of Hawkes process. The proposed method is applied to the five-minute high frequency data of the Chinese stock market. The empirical results show that, for the two main Chinese stock markets, only background intensity is significant in the Shanghai stock market, while mutually exciting effect is significant in the Shenzhen stock market. Both the location and size of jump in the Shanghai stock market have significant stimulation to the next occurrences of jump in the Shenzhen stock market. Furthermore, the proposed model performs very well in predicting the future jump tail events.

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