Improvement Of VPNN Model And Prediction Of Market Liquidity And Volatility

As high-frequency trading(HFT)becomes more and more widely used in quantitative investment,its proportion is also increasing in the international market.Many traders take advantage of the rapid nature of HFT and take advantage of the price difference over time delay to make a small profit.In China,due to the T+0 trading rule in the futures market,high-frequency trading has also received more and more attention from traders and scholars.With the development of HFT,HFT risk research has gradually become a hot topic of discussion,such as the "May 6 flash crash" in the United States in 2010 and the "8.16 GuangDa WuLongZhi" incident in China.From the micro perspective of the financial market,Easley et al,a foreign scholar,analyzed the impact of information asymmetry in transactions on trading products and established a series of relevant models to measure market risks by observing information changes in transactions.On the contrary,our domestic research on this is relatively limited.Against this background,this paper USES information model to measure the asymmetry degree of market trading information,and USES the index value of VPIN model to build a risk prediction model of high-frequency trading for comparative analysis.Firstly,in the theoretical research of VPIN model in the market micro structure model,the model is partially optimized according to the actual data of the domestic market.The first is to use the sigmoid function with adjustable parameters to replace the standard normal distribution function in the calculation process of dividing the trading volume in the model.Instead,the arithmetic average is replaced by an exponential average when calculating the final VPIN value,on the principle that new information should have more weight based on the timeliness of the information.The four models are compared.Then,this paper USES the 1-minute high-frequency data of HS 300 stock index futures,determines the parameters of sigmoid function according to the accumulative distribution of standardized price of sample data,and determines the average weight of the index according to the correlation between VPIN obtained by different weights and the absolute return of the next period.Secondly,in the empirical aspect,the short-term prediction ability of the four models on abnormal events in the financial market was compared and analyzed for a specific time.It was found by the final statistical analysis that the index value of the improved EXPS_VPIN model was significantly higher than that of the VPIN model before the occurrence of abnormal events,and was close to the highest level 1.And after the event,the EXPS_VPIN model can quickly restore to normal value,while the ordinary VPIN model does not quickly restore the average value.Finally,aiming at the risk research of high-frequency trading,this paper comprehensively considers the short-term liquidity and volatility level of the market,and selects the corresponding liquidity and volatility indicators to build the risk prediction model.In the model,the control variable method is used to explore the relationship between the index values of different models and the liquidity and volatility of the next phase.After parameter estimation and hypothesis test of the model,the experimental results show that the four models play a certain role in the risk early warning of HS300 futures market in China.Starting from the risk prediction of high-frequency trading,this paper makes use of the financial market micro structure theory model,through the improvement of VPIN model and the empirical study in China's stock index futures market,to enrich and develop the application of VPIN model in China's market,and has certain reference significance for investors and regulators.