Stock index futures are a financial derivative developed in 1980s and have the largest transaction size in the world. As an important financial innovation, stock index futures play a vital role in the process of developing a stock market. It can be used to hedge market systematic risk caused by spot price fluctuations. However, the hedging function of stock index futures is dependent on the determination of hedging ratio. Determining the hedging ratio appropriately by hedging model of stock index futures can improve the effectiveness of hedging and avoid the risk of stock market effectively. Therefore, the motivation of this paper is to research on dynamic hedging model of stock index futures using modern risk measurement technology.There are four sections in this paper. Section 1 reviews literature on hedging models using modern risk measurement technology and analyzes the disadvantages of VaR and advantages of CVaR. Section 2 establishes dynamic hedging model of stock index futures based on the Conditional Value at Risk. Section 3 empirically examines the data of Shanghai and Shenzhen 300 Index Futures simulation trading, and calculates the dynamic adjustable hedging ratios. Section 4 concludes this study with major findings and contributions.The research uses the method of Conditional Value at Risk to measure the risk. The common method of Value at Risk ( VaR) in modern risk measurement technology does not take into account the tail risk in the event that loss exceeds VaR, and therefore the information provided may mislead investors. An alternative measure named Conditional Value at Risk (CVaR ) , which can make up the shortcoming of VaR, is used to study the tail lose of hedging problem. With the objective function of minimum Mean-Conditional Value at Risk (Mean-CVaR), a dynamic hedging model of stock index futures based on Conditional Value at Risk is established, and hedging ratio is determined.The main specialties and contributions of the model lie on two aspects. For one thing, the impacts of confidence level and variable trading fee on optimal hedging decision are examined, which make the model more perfect. For another, bivariate error correction time-varying conditional correlation GARCH model is utilized to estimate hedging ratio. The advantage is the fact that it not only considers the co-integrated relationship between stock index futures and spot price series but also better fits the features of heteroskedasticity and time-varying correlation coefficient existing in return residual series.The results show that the investors with different risk bearing capacity should adopt different optimal hedging strategies. In other words, the investor with higher degree of risk aversion should use the hedging ratio with higher confidence level, while the investor with stronger degree of risk preference should use the hedging ratio with lower confidence level. Besides, the minor adjustment of trading fee can achieve the goal of controlling the volatility of slock index futures market. The controlling measure is so moderate small negative effect that it will not lead to the severe fluctuation of the emerging Chinese stock index futures market.In conclusion, the dynamic hedging model of stock index futures based on Conditional Value at Risk is established. The model considers the impact of confidence level and trading fee on hedging strategy. We also obtain timely and dynamic adjustable hedging ratios to trace and control risk based on the hedging simulation and empirical calculation on Shanghai and Shenzhen 300 Index Futures simulation trading.%本文建立了基于最小化均值-条件风险价值( Mean-CVaR)的股指期货动态套期保值模型.模型的主要特点与贡献在于两方面:一方面,考察了置信水平和可变交易费用对最优套期保值决策的影响；另一方面,利用二元误差修正的时变条件相关GARCH模型估计套期保值比率,优点是不仅考虑了股指期货与现货价格序列之间存在的协整关系,而且更好地拟合了收益残差序列存在的异方差性与相关系数时变性的特征.最后通过对我国沪深300指数期货仿真交易的套期保值模拟与实证测算,得出能够动态调整的股指期货套期保值策略以实时追踪与控制风险.
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