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The Empirical Research On The Price Of Hu-shen 300 Stock Index Futures

Posted on:2017-11-21Degree:MasterType:Thesis
Country:ChinaCandidate:Q W ChenFull Text:PDF
GTID:2359330512475740Subject:Quantitative Economics
Abstract/Summary:PDF Full Text Request
Along with the deepening of financial innovation and gradual adjustment of the structure,China’s first financial futures,called HS300 index futures,launched on April 16,2010,it was based on Hushen 300 Index and connected with capital market.Before that,China’s stock market is just a unilateral do much of the market,investors in the stock market have only one way to profit,namely buy stock in low price and then sell them in high price,get profit through the difference of price.For market development,the model has a great deal of defects.There are results indicate that a sound market should include the short-mechanism.The lack of short-mechanism will result to that the market share price is generally overrated.After the launch of the csi 300 index futures contracts,our capital market really set up to do more with the shorting mechanism and make an historic leap,the index futures became tools of hedge systemic risk of stock index futures,makes derivatives innovation get attention.In recent years,trading volume of the stock index futures market is much higher than the stock index spot market.With the reform and innovation of China’s financial market,the stock index futures market in China has entered the fast development period,theory of stock index futures research and exploration,especially in Hushen 300 index futures price forecasting,has become one of hot topics which has important practical significance in financial.ARMA--GARCH model is established in this paper,make relatively comprehensive and deeply research to forecast,in order to help investors in avoiding risk,increasing revenue,making decision in investment,providing advice for the stock index futures investors,riching theory of research in predicting the price of stock index futures,has a certain practical and theoretical significance.In the perspective of futures investment,the article take log-return of closing price of consecutive contracts of Hu-Shen 300 stock index futures from April 16,2010 to July 29,2016 as object,with the aid of ARMA--GARCH model,from two angles which is high frequency cycle and low frequency.Through the establishment of ARMA-GARCH model and comparing prediction effect under different distribution,the paper found that ARMA(4,4)--GARCH(1,1)is applicable both in short period and long period,and that the root mean square error of the peace party under absolute error short cycle model is smaller,the effect of prediction is superior to the long period.Innovation of this article lies in that we analyze the predictability of object in variance ratio test,which means exploring the fact that if the stock index futures price meet to the random walk in front of the building forecast model.Then we carry on the comparison and analysis of the t distribution and normal distribution,GED distribution from two angles which is high frequency cycle and low frequency.build model has potential replication.The shortcomings of this paper:(1)because the csi 300 stock index futures are influenced by the subject matter of the csi 300 index,the model and the precision of prediction which is setted up in the paper also is affected by the stock market.The Chinese stock market is affected by the national policy fluctuations more than foreign mature capital market,and our country for the stock market volatility limit set limit up and down for crude,under the precondition of the two,a simple linear GARCH model reflects the market volatility is not necessarily the most real;(2)the variable selected in the article is the csi 300 index futures daily closing price,thus lost the wave analysis of each trading day,the loss of the part information,leading to final results reflect the comprehensive enough;...
Keywords/Search Tags:ARMA-GARCH, Price forecasting of futures, The high frequency data and low frequency data
PDF Full Text Request
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