| April16,2010, we launched the Shanghai and Shenzhen300stock index futures. The CSI300Index Futures is a financial derivative products tailored for China’s capital market. For domestic investors, especially institutional investors, the launch of the CSI300stock index futures to provide them with a reasonable and effective risk management tools. Because through the years of practice experience, we prove that the Index Futures can effectively avoid the risk of the stock market. During the hedging transactions, how to determine a reasonable hedge ratio is important because the hedge ratio can cause different hedging effects. Most of the previous studies were concentrated in the static hedging model, but this paper is to study the dynamic hedging model. This article is mainly in the framework of VaR as the objective function, the use of multivariate GARCH model to calculate the optimal hedge ratio so as to achieve the purpose of dynamic hedging.The first chapter focuses on the background research of the dynamic hedging of the stock index futures, the research purpose and the related literature review. By summarize previous research literature, we found that most of the studies in the past for stock index futures hedging analysis were under the minimum variance objective function. The application of this model were the basic econometric analysis model, a major feature of these models was that they are extremely harsh requirements for data, so a lot of analysis of the data is difficult to achieve. Once the analysis cannot meet the requirements of the model, the calculated results may have some systematic deviation of potential problems for the end result. There have many research can do under the VaR objective function, Chi guotai (2008) proposed to determine the optimal futures hedge ratio based VaR principle, their study found that when the expected return of the futures contracts completely related or VaR confidence level close to100%of the yield rate of zero, the futures close to the minimum variance optimal hedge ratio based the VaR determined Futures optimal hedge ratio unlimited.The second chapter discusses the optimal hedge ratio under different objective functions; this article is mainly concentrated in the minimum variance model and the VaR model. The study found that VaR objective function and minimum variance hedge ratio has a completely different nature, variance minimization hedge ratio just for risk-averse investors, VaR objective function takes into account the investor’s speculative demand, because in certain circumstances, VaR objective function can be transformed into optimal hedge ratio under variance minimization model, so VaR objective function has greater applicability. It is mainly the introduction of the futures expected rate of return, it can be seen from the last expression, when the expected rate of return is positive, that is, when investors expect the stock index futures to rise, then the expected rate of return is calculated optimal hedge ratio is smaller than the classic model to calculate the optimal hedge ratio. This phenomenon can be explained in the real market, when investors expect the market will rise, they are generally in such situation, investors have expected there will be a cash income in the future, but investors Also want to enter the market now, but they do not have the cash, so investors can buy a certain amount of stock index futures close to the time of arrival of the cash. Instead, when investor expectations of future stock market yield is expected to be negative, that is, the future of the stock market will fall, then the expected rate of return is negative, then the expected rate of return is calculated optimal hedge ratio is bigger than the classic model to calculate the optimal hedge ratio. This phenomenon can explain the same in the real market, when the market outlook is expected to fall, they will be hesitant to sell the stock, but some investors are worried about their own misjudgments, especially When investors to hold the stock has some revenue.Chapter three mainly empirical analysis variance minimization model as the objective function and VaR hedge ratio model, from the results of the empirical analysis, the result calculated by VaR model and minimum variance optimal hedge ratio were not very large, this may be due to the selected data in this article, because there has a very strong correlation between the CSI300index futures and CSI300stock index, and the relationship is almost linearly related, so there results are not that much different, but in view of the results of the calculation, the VaR method of optimal hedge ratio will give hedgers and investors another chose, if the investors were risk-averse, then the optimal hedge ratio of VaR approach can translate into minimum variance optimal hedge ratio. The operation of the stock index futures will need a great amount of money, the VaR method also has a feature that to save the cost of hedging transactions, because in the expected rate of return being the case, the VaR method of hedging the ratio will be less than the minimum variance hedge ratio, so investors can save the cost of trading margin.Chapter four analyses the significance of Dynamic Hedging from analysis the lack of static hedging theory, stock index futures, and finally introduced the Dynamic Hedging theory of stock index futures. First, the paper analyzes the hedging ratio model through the empirical analysis of the third chapter, continue to choose VaR hedge ratio model as the objective function. By adding the time variable, it is easily to change the model of the hedge ratio to a dynamic model. The Hedging involves two variables, so this paper will use binary GARCH model. CSI300stock index futures and the CSI300index distribution is not normal distribution. The have fat tail characteristics, with the majority of financial sequence with a certain degree of skewness in Chapters Ⅲ and Ⅳ analysis are based on the normal distribution to fit the distribution yield, which is clearly inappropriate. Therefore, this chapter wills introduction SKST distribution. Yield fluctuations fitting selected the DCC-ECM-BGARCH model, the DCC model is relatively easy to estimate. Introduced in the mean equation ECM reasons found in the third chapter in the empirical analysis of the logarithm of the CSI300index futures yield and CSI300stock index cointegration relationship exists between the numbers of yield, and therefore select the DCC-ECM-BGARCH models to fit the yield with time-varying characteristics.Chapter five Empirically Analysis of the dynamic hedge ratio, the results shows that some dynamic hedge ratios are greater than1. This can be explained in the actual operation, when calculated static hedge ratio are taken some time span of data to be calculated, but also in order to ensure the applicability of the optimal hedge ratio, the time span selected longer than that, from the analysis of the long-term view, the price volatility of the stock index futures will be greater than the volatility of the spot price. In this chapter, however, the optimal hedge ratio is based on the time change, which cannot be ruled out fluctuations in the spot price is greater than the in a subparagraph time within the price fluctuations of the stock index futures. In the general case, investors feel that because of our unique trading patterns, the stock market is the T+1trading patterns, and stock index futures market is trading patterns of T+0, then the stock index futures would surely stock market were more intense than the stock market, and therefore the optimal hedge ratio should be less than1, but the results of this paper, the hedge ratio is greater than1. However, in the contrary, precisely because of China’s stock index futures market can quickly react to market information, so investors in the stock market is likely to use the information provided by stock index futures speculation in the stock market, so that there may be fluctuations in the spot market in a short period of time greater than the volatility of the futures market. On the whole, the end of this article calculated optimal hedge ratio is greater than one, because the price discovery function of stock index futures, the spot market to market information can improve reaction mode, there will be a period of time, the stock index futures Price volatility is less than the volatility of the spot. |
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