Research On The Optimal Hedge Ratio And Performance Of Futures Market Of China

After 20 years of development, the functions of China’s futures market are more and more sophisticated. There are two functions of Futures markets, price discovery and risk aversion, and risk aversion function is an important function of futures market, which achieved through hedging. The essence of hedging is through the operation of futures contracts to hedge the spot market in the futures market. Hedging is of great significance on the futures market and the market economy. It is the basis for futures markets, and it is a necessary condition for the development of futures markets. Hedging can be effectively promote commodity production in the spot market, and it also provided a guarantee of economic stability. For enterprises, hedging is an important risk management and price management tools.In this paper, qualitative analysis and quantitative analysis method are used. We give more comprehensive study of the hedge ratio and its performance. First, clear the theory of hedging and then use model to analyse hedging. In the process of solving the minimum hedge ratio, gradually deepened from static to dynamic. After the analysis of empirical data, we draw conclusions.There are five sections in this paper:The first chapter is the preamble, in this section, the author give the clear object of study, research purposes and the literature at home and abroad which show the research history of futures hedging and research status.Chapter II is part of the basic theory of hedging. In this chapter, first reviewed the development process of hedging, and then discusses the general operating principles of hedging and hedging applications, followed by discussing the effectiveness of basis and hedging, and then clear the risks and costs of hedging, and then discusses the characteristics of the hedging transaction and its development. At last, we discuss hedging in Chinese futures market.The third section is to determine the optimal hedge ratio, belonging to the theoretical part of the building model. In order to solve the optimal hedge ratio, the OLS, B-VAR, ECM, BEKK-GARCH methods are introduced.The fourth chapter is an empirical research of aluminum futures. First according to the distance to the current day selected six sets of data, and separately examined the relationship between the spot price. Taking into account the liquidity of the futures markets, the final choice a time series for hedging. We use the OLS, B-VAR, ECM, BEKK-GARCH method for solving hedge ratio and then discuss their effects respectively.Chapter V is a summary and policy recommendations. Combined with the article research results, we give advice for hedging and futures regulators. Finally the author gives policy recommendations on how to develop futures market and improve the hedging function.Although the author has done a lot of work, but because of data and personal capacity constraints, there are still some shortcomings in this article:First, the author uses familiar models to discuss the hedge ratios and the performance, but the paper is inadequate on how to use more sophisticated models.Second, the author does not take into account the cost of hedging. there is a transaction cost in the actual operation, such as commissions, transaction fees, margin interest, etc. The existence of transaction costs would not only affect the choice of hedging method, it will also affect the measure of hedging performance.This research focuses on the next step:First, establish the optimal hedging based on nonlinear models, although models based on the linear correlation of the hedge ratio can achieve good results, related models based on nonlinear reflect the characteristics of financial time series better. Second, put transaction costs into the hedge ratio and hedging performance models to make it more practical.