Research On The Impact Of High-frequency Trading In My Country's Stock Index Futures Market On Market Risks And Risk Early Warning

Stock index futures is an important financial derivative and its main function is to maintain capital values and avoid systematic risk in the stock market.Since China's first stock index futures,CSI300 stock index futures(IF.CFFEX)was published in 2010,China's stock index futures market is now in a period of rapid development.Because the stock index futures market is closely related to its stock spot market,coupled with the principle of leveraged trading of futures,the marginal risk of the stock index futures market may cause risks in the spot market through the risk transmission mechanism.Meanwhile,with the rapid development of trading technology,the capital market has begun to show the trend of programmatic trading and high-frequency trading(HFT),which poses new challenges to the risk management and pre-warning of China's stock index futures market.Due to the leverage characteristics of stock index futures,cross-market correlation characteristics,high-frequency trading and other factors,it greatly increases the influence of risk explosion and difficulty of regulating on stock index futures.At present,relevant research on HFT in China's stock index futures market is still scarce,and systematic research work has not yet been carried out.Based on the actual situation of China's stock index futures market,this thesis deeply investigates the high-frequency trading behavior patterns and identification methods in the market and analyzes the relationship between HFT and market volatility and market liquidity risk.The market risk warning method under the background of HFT is proposed,which provides theoretical and methodological guidance for strengthening market risk management and regularization,exerting positive effects on HFT on the market,and actively guiding the healthy development of stock index futures market.Firstly,based on the real market trading data,this thesis empirically analyzes the trading behavior patterns of high-frequency traders in China's stock index futures market,compares the behavior patterns of high-frequency traders under different trading rules,and proposes the HFT identification method based on co-clustering methods applied in China stock index future market.Compared with the previous identification methods based on manual-defined rules,the method proposed in this thesis benefits good adaptability and accuracy.Specifically,this thesis uses the 500-millisecond transaction data to conduct empirical experiments,analyzes the key characteristics of high-frequency traders in the stock index futures market in terms of delays and the number of withdrawal.To avoid the limitations of HFT identification based on fixed threshold rules,this thesis uses the unsupervised machine learning algorithm of co-clustering method to analyze the investors' behavior in the stock index futures market.The experimental results show that the proposed method can effectively identify different behavior patterns of different trading roles and meanwhile filter out the instantaneous effect on the behavior pattern recognition.Therefore,the proposed method has better applicability under different market conditions,and facilitates the identification and supervision of HFT behavior by regulatory agencies.Secondly,this thesis selected the minute-level high frequency trading data and make an in-depth analysis of the relation of high-frequency trading and the volatility of China stock index futures market.Compred with previous research work on volatility,this thesis studies the relation of HFT under different regulatory policies and different market conditions.Specifically,based on the intraday volatility metrics,this thesis analyzes the intraday characteristic changes of China's stock index futures before and after the restricted policy of stock index futures,and uses the vector autoregressive model(VAR)to demonstrate the positive correlation between HFT activiveness and volatility.And then,for the purpose of illustrating the market volatility,the GARCH model was introduced.The experimental results show that owing to the HFT is reduced when stock index futures is limited,the overall market was much more affected by the older information than newer ones,and the market volatility is generally characterized by long memory and persistence pattern,and the market has experienced abnormal fluctuations.Thirdly,based on minute-level HFT data,an investigation is carried out on how HFT affect our stock index futures market on its liquidity and the liquidity adjusted value at risk(LVa R).Compared with other researches,this thesis is mainly focusing on HFT trader's preference on the liquidity of Chinese market,as well as the relationship with HFT and LVa R.Specifically,we defined high frequency trading proxy index(HFTPI).By VAR model on HFTPI and Relative Spread,we gain the conclusion that HFT can cause fewer relative spread,effective spread,adverse selection and larger realized spread.It means the HFT trader will strategically enter the market when the trading cost and information asymmetry is relatively low.Furthermore,we find the LVa R of the future market has negative relationship with HFT in the second part of our research.Meanwhile,we also find the average proportion of market exogenous liquidity risk(ECL)in overall LVa R rose from 4% to 16.9% after the market was restricted.HFT under extreme condition is also researched with the conclusion that HFT under extreme trend won't result extra ECL.Last but not the least,based on minute-level and tick HFT data,we researched the risk warning method on stock index market under HFT.Based on Volume-Synchronized Probability of Informed Trading(VPIN),improvement is made to fit this method more suitable for the HFT environment.Specifically,this thesis expounds the original VPIN index,as well as,its improved calculation process,and proves the effectiveness of the improved VPIN model through detailed comparative experiments and empirical analysis.Finally,we find the improved VPIN made more accurately warnings on market fluctuations than the traditional method.We also achieve good market abnormality warning in the period with highly active HFT.In addition,this thesis also discusses the relationship between the improved VPIN with market volatility and market LVa R.The empirical evidence shows that when market volatility and LVa R rises,VPIN can respond quickly.Meanwhile,when the market is under extreme trend,our method can still warn effectively,but the frequency of early warning trigger is obviously higher than that of non-extreme trend.The results of this research show that HFT is a neutral tool that plays a positive role in promoting market liquidity,while the increment in HFT will also increase market volatility,thereby arising market uncertainty.Meanwhile,HFT behavior can be identified through technical methods,and market risks can also be well alerted in some instance.From this point of view,in order to maintain the long-term healthy and stable development of China's stock index futures market,we have two recommendations: First,regulatory authorities should allow the existence of HFT,and appropriately relax the restrictions of HFT.Secondly,it is necessary to formulate more scientific and accurate HFT judgement standard,establish a complete HFT monitoring system and early warning mechanism.In order to maintain the bottom line of systemic financial risks,it is higly recommanded to imporve the supervision ability to prevent abnormal market fluctuations as well.Overall,in this thesis,we discussed and gained initial results on HFT active pattern,the market risk influence of HFT and risk warning method under HFT.However,due to the young born of China's stock index futures products,the market is still in high-speed growth,and HFT is becoming more and more complicated.In addition,there is a high correlation between stock index futures and the spot market.Therefore,there are still shortcomings in this thesis.For future research,it is necessary to study the cross-market HFT behavior model and the cross-market risk transmission mechanism to provide further theoretical and method guidance for the healthy and orderly development of China's stock index futures products and financial derivatives markets.