Abstract
The impact of successive jumps in price process on volatility is very important. We study the nature of self-motivation in price process using data from China’s stock market. Our empirical results suggest that: 1) Price jumps in China’s stock market are generally self-motivated, i.e., price jumps are clustering. 2) The jump intensity of China’s stock market is time-varying, and follows log-normal distribution, which indicates that the jump intensity is asymmetrical. 3) The jump intensities’ sequence exhibits typical long memory.
Highlights
With the availability of high-frequency data, the study of dynamic behavior of volatility based on high-frequency data has become a focus of econophysics
In [15], the self-motivation of price jump is determined by a time-varying intensity process which can be expressed as a differential equation of time and counting process
We choose the CSI300 index and 23 stocks that are randomly selected in the constituents of CSI300 index as the representative of China’s stock market, and take the 1-minute high-frequency price data of 2015, which is highly volatile in China’s stock market, as samples, and resamples into 5-minute return series to conduct empirical analysis such as jump test and jump self-motivation test
Summary
With the availability of high-frequency data, the study of dynamic behavior of volatility based on high-frequency data has become a focus of econophysics. The clustering and long memory of volatility are generally considered to be related to the jumps in the price process. The research focus of price jump has shifted from jump test to jump correlation [7]-[14]. We use the jump self-motivation test [16] to study the phenomenon of price jump in China’s stock market, and focus on the long memory of the intensity process of price jump. We find that the intensity of price jumps is generally long-memory in China’s stock market. This explains the phenomenon of self-motivation between price jumps, and deepens our understanding of the phenomenon of violent volatility in China’s stock market. Appendix details the kernel database of statistics generated in our framework
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