| The risk of the stock market mainly includes systematic risk and non-systematicrisk. The non-systematic risk can be avoided by asset portfolio, the systematic riskcan’t be avoided by asset diversification. On April16,2010, the Shanghai andShenzhen300index futures was officially listed in China’s financial exchanges. Theindex futures has the hedging function, which can avoid the systematic risk of thestock market. The core of futures hedging is the determination of hedging ratio. Theexisting research of the futures hedging ratio models mainly divided into statichedging models and dynamic hedging models. According to the study of hedgingratio models of domestic and foreign scholars, they found that the dynamic hedgingratio models are better than the static hedging ratio models. Currently, the dynamichedging models are mainly estimated by the mean-GARCH models, which are mostlybased on the low-frequency data. With the developing of computer technology, thehigh-frequency data is more and more obtained. Compared with the low-frequencydata, the high-frequency data contains more market information, and with theincreasing of sampling frequency, the high-frequency data obtains more and moremarket information. So, the paper will empirically study the hedging models ofShanghai and Shenzhen300index futures, in order to build suitable hedging modelsfor the emerging market.The paper firstly studies the asymmetry and jump of the price volatility of theShanghai and Shenzhen300index and the index futures, and statistical property of theprice and the basis. On the basis of using the multivariate realized kernel to modifythe realized volatility matrix, the paper builds the mean-conditional autoregressiveWishart models and the mean-conditional autoregressive Wishart-heterogeneousautoregressive model, and analyzes the random property of the models. Then, thepaper obtains the analytical expression from three objective functions, which are theoptimal variance, the optimal portfolio VaR and CVaR. In the analytical expression,we need estimate the conditional mean and conditional covariance matrix of the spotand futures. Finally, the paper will adopt5minutes high-frequency data of theShanghai and Shenzhen300index and index futures to evaluate the hedgingefficiency of the mean-CAW-HAR model, the mean-CAW model and themean-BEKK model, which mainly on the basis of the portfolio risk minimization, theutility maximization and the VaR values of returns. The paper draws the followingconclusions.Firstly, the price of the Shanghai and Shenzhen300index and index futures arebasically same. They have the cointegration relationship, which is advantaged to theindex futures hedging. Secondly, the price volatility of the Shanghai and Shenzhen300index and indexfutures are asymmetry and jumping. The asymmetry volatility of the Shanghai andShenzhen300index is more than that of the index futures, and the jump of indexfutures is higher than that of the Shanghai and Shenzhen300index. This explains thatthe volatility of index futures market is greater than that of the spot market.Thirdly, comparing the mean-CAW-HAR model and the mean-CAW modelwhich use the realized volatility matrix with the mean-BEKK model which uses theyield sequence, the paper finds the hedging efficiency of the mean-CAW-HAR modelis the best, following by the mean-CAW model, and the mean-BEKK model is the lastone.Fourthly, comparing the hedging efficiency of the optimal variance value, theoptimal portfolio VaR value and CVaR value, the paper finds the hedging ratio andhedging efficiency of the optimal portfolio CVaR are steady, and gradually tending tothe hedging ratio of the optimal variance with the increasing of confidence level.When considering the utility maximization and the portfolio return VaR value, thepaper finds the hedging efficiency of the optimal portfolio VaR is the best. Whenconsidering the risk minimization, the hedging efficiency of the optimal variancevalue and portfolio CVaR value is the best. |
PDF Full Text Request