Research On Stock Index Futures Hedging Based On Copula-CEVaR Model

Having a good risk management function is one of the important functions of the stock index futures. Since the2008financial crisis and the2011European debt crisis spread, the financial market traders demands for greatly increased risk aversion. But the stock index futures and other derivative products which suffering the strict supervision demonstrates the enormous vitality. In April2010, the SHSE-SZSE300issued, to provide investors a more extensive hedging instruments and hedging tools.However, due to China’s financial market is not mature, appeared for a short time, the current hedging empirical analysis of stock index futures is not very deep. At early time, the researchers used of various types of linear correlation coefficient to measure the correlation between random variables. But it can’t portray the true correlation well between variables. With GARCH model to describe the fluctuations yields, ignore the many restrictions of GARCH model. Another example is the distribution of financial returns are usually rendered fat tail, spikes and asymmetric features, traditional research often assumed the rate of return subject to normal distribution assumption. The true relationship by using Copula function to measure the correlation coefficient between the nonlinear stochastic variables, It also can fit the true relationship between the variables better. By the use of Cornish-Fisher expansion can get the distributed correction yields; eventually deduce the most excellent hedge ratio using VaR model, comparing with the traditional method, the hedging effect, the accuracy and applicability of the model have a huge upgrade by using this method.This paper studies the problem of SHSE-SZSE300hedging. It is divided into five chapters discuss:The first chapter is the introduction. In this section, I introduced the research background and significance, and combed the research status and development trends at home and abroad this topic. By reviewing research results, I further clarify the research ideas and research framework and lack of innovation and finally introduced in this paper.The second chapter is an overview of the basic theory Copula function. Introduces several common basic definitions and properties of Copula function, and then introduce the correlation coefficient of Copula function between variables. By introducing non-parametric kernel density estimation, elaborated the parameter estimation method of Copula function.The third chapter is to derive the optimal hedge ratio based on VaR model. By introducing the Cornish-Fisher expansion type, obtain the minimum variance hedge ratio, the optimal hedge ratio under normal and non-normal distribution under optimal hedge ratio of expression.The fourth chapter is empirical analysis. This part is the core of this paper. This paper selects and processes the price data of SHSE-SZSE300index and stock index futures market, respectively, consider hedging costs or not, with the sample data and out of the sample data to calculate external verification, get the optimal hedge ratio under every model and explain the changes of hedging effect during every stage about improving model. The empirical results proved that the result of this paper can improve the effect of the hedging model greatly.The fifth chapter is the conclusion of the analysis and policy recommendations. This part of the paper concludes according to the analysis, elaborating the correlation between risk and returns, drawn on investment operations and on the policy-making relevant recommendations.The innovation of this paper:Using Copula function to measure the correlation of random variables, measure of nonlinear correlation between variables better, the tail can better characterize the distribution of the relevant variables.Since the distribution of financial data is usually biased, for example, fat tail, spikes and asymmetry. This paper uses Cornish-Fisher expansion to correct the distribution of portfolio returns. To make it more in line with actual market rate of return.In a sense, the hedging combination is a group of asset portfolio. This paper takes into account both the risk of hedging combination, but also considers the impact of expected rate of return generated by the hedging combination.Combination of factors to consider hedging transaction costs during the construction. So the final hedging portfolio is more realistic and more practical significance.Of course, because of my limited level, there are many deficiencies in this article:Over a period of time, due to the volatility of the capital markets, in order to adapt to the volatility of capital markets, have a better protection of risk positions, and also reducing unnecessary transaction costs and holding costs. About this problem, we can consider to build dynamic hedging strategies to improve it.From the view of research methods, ignoring asset price variance dynamic random may lead to the effect of hedging distortion. Also, the good news and the bad news have different impact to the yields, in the other word, the impact of market news has asymmetrical effects. To solve this problem,we can consider using GARCH, EGARCH and other models for some improvements, but sometimes the return series itself has no ARCH effect, we can’t use the above model directly. This problem will wait for the future to have further research and analysis.