A Study On Testing And Triggering Of The Jump Behavior For Intraday Asset Price In China Stock Market

The jump behavior for financial asset price may have direct or indirect impact on estimating and forecasting volatility of the asset's return, option pricing, market risk measurement and management, and asset allocation. At present, the research of jump behavior for financial asset price has made noticeable achievements, mainly displaying in:from the use of low frequency data (day, week and month) to the use of (ultra) high frequency data (time-sharing data, tick by tick data, transaction-to-transaction data); from the use of the parametric methods (such as GARCH-JUMP, SV model) to the use of the nonparametric method(such as BNS research system, TMPV research system); from never considering the impact of noise microstructure on the jump behavior to pay more attention on its impact; from considering the information stock or liquidity stock separately to considering both of these impact on jump behaviors. On this basis, we construct an intraday jump test framework which is suitable for (ultra) high frequency financial data, which is inspired by the pre-averaging method and Anderson, Bollerslev, Frederiksen et al. (2010) (Abbreviation:ABF jump test). We form a relatively complete analysis framework and establish the jump behavior mechanism theory model based on macroeconomic information, liquidity and lag information. Finally, we use the Shanghai 50 index and its constituent ultra-high frequency data to investigate the jump behavior of Chinese stock market. The result are valuable for supervisors and investors.The main research content of this paper are as follows:Firstly, we review the existing domestic and foreign related research about jumping behavior (including the theoretical research and empirical research) and summarize disadvantage of previous studies, which mainly displays in:1) most of research mainly concentrated in the daytime jump behavior; 2) The use of financial data is insufficient (most focus on 5 minutes and more low frequency data); 3) for triggering mechanism about jump behavior research, either consider macroeconomic information impact or liquidity impact, and never to consider both these information.Secondly, we form a research framework about jump behavior which is suitable for (ultra) high frequency financial data, and it is based on ABF intraday jump test proposed by Christensen (2014) and Zhao Hua (2014). For jump testing, combining the pre-averaging method and ABF intraday jump test, we estimate realized volatility and Bi-power Variation and form an intraday jump testing method; For trigger mechanism, taking into consideration of macroeconomic information, liquidity and lag information, we establish theoretical model based on logistic model from the perspective of individual stocks data model and mixed data model.Thirdly, we use the Shanghai 50 index and its constituent ultra-high frequency data to test the jump behavior and analyze the distribution characteristic of the jump behavior, which mainly includes the basic features, weekly distribution, monthly distribution characteristics and intraday distribution characteristics, etc. Generally speaking, jump behavior often happen on China's stock market, and there may be multiple jump behavior in a single trading day, jumping frequency basically maintain within 5 times, but the probability of single jump is relatively larger; Positive and negative jump distribution is asymmetry, and probability of positive jumping is greater than negative jumping; Jumping strength changes in month and industries; Besides the ecological-economic which presents "L" shape distribution characteristics, other industries'jump behavior presents "U" shape distribution characteristics; from 10:00 a.m. to 14:50 p.m., jump intensity distribution is relatively uniform, and jumping strength and its variation is relatively small.Finally, based on macroeconomic information and liquidity and lagging information, we use both individual stocks data and hybrid data to investigate triggering mechanism of jump behavior. The results indicate that compared with the macroeconomic information, liquidity shocks are more likely to lead to jump behavior; the six indexes (include CPI, PPI, M2, PMI, macro information releases and depth and width) are easy to cause jump behavior of constituent stocks price; Four macroeconomic indicators:CPI, IO, M2, PMI, have a significant effects on the Shanghai 50 index jump. Generally speaking, it will be able to react completely from 10 minutes to 30 minutes after the information releases; In addition to the financial industry, the other seven industries only react to part of information (including macroeconomic information and liquidity information). The CPI, M2. PMI is closely related to various industries'jump behavior; Liquidity shocks'pattern for trigger mechanism of jump behavior is similar but different between various industries, the width is more likely to cause jumping.The innovation of this article is mainly manifested in two aspects:Firstly, we form a relatively complete research framework about intraday jump behavior which is suitable for ultra-high frequency financial data, including the testing and triggering of jump behavior for intraday asset price, the analysis of the empirical results is also more deeply and refined. Specifically displays in:1) we use ultra-high frequency financial data which contains the most abundant market trading information to analyze the result from multiple points of view, including basic distribution features, weekly distribution, monthly distribution, intraday distribution characteristics.2) Taking into consideration of macroeconomic information, liquidity information and the lag information, trigger mechanism is discussed from the perspective of individual stocks data model and hybrid data model. Overall, the study of trigger mechanism is more comprehensive.Secondly, for the selection of key parameter ?, we use ultra-high frequency data in China stock to compares different ? based on two criteria: and. The conclusion is as follows:in China stock market,0.8 to 2 is the reasonable choice range. In the empirical analysis, we set ? equal to 0.8,1, and 1.5 three conditions to do sensitivity analysis. In order to prevent adverse effects caused by factors such as smoothing are insufficient or excessive smoothing, we set ? equal to 1.