Research On Hedging Of Stock Index Futures In Investment Portfolio Management

The financial derivative-Stock Inedx Futures Contract emerged with the increasing demand to avoid systematic risk in financial market.In view of the development during the past twenty years or more from its birth,the product is proved to be a tremendous success.After a long-time stagnation in the growth of Chinese financial derivatives,the curtain rises at the time when the Chinese Financial Futures Exchange was established in September,2006.The theory and practice circles pay much attention to Stock Index Futures Contract which is the first financial derivative authorized after the establishment of the Exchange.Furthermore, the study in this field has come to bear fruits abundantly in recent years.The short mechanism possessed by Stock Index Futures Contract changes the unilateral profit mode in the case that investors can only profit when stock price goes up.Consequently,the research on portfolio optimization strategy is one of the central issues.The effective portfolio optimization can help the investors to gain low risk earnings even if they hedge with Stock Index Futures Contract.Secondly,confronted with a large variety of Stock Index Futures Contracts in the coming years,the investors should learn how to measure different contractual risks and then hedge with proper contract,so the research in this regard is quite worthwhile.Finally,the study on the core of hedge research-i.e,the optimum hedge strategy can provide theoretic support in improvement of hedge efficiency and its application in investment practice.Taking the hedge research of Stock Index Futures in investment portfolio management as the entry,we make some study on portfolio optimization,contractual risk evaluation and optimum hedge strategy respectively.The frame and main innovations of this dissertation are described as follows:In Chapter One,the research and development of Stock Index Futures market are summerized.In Chapter Two,based on Unsupervised Learning Theory,in order to avoid potential investment risk and realize portfolio optimization,with Quantile Regression Model and Change Point Test,we detect outlier transaction behavior,and then recognize and eliminate timely abnormal fluctuation of stock price when we construct stock pool and adjust investment portfolio by analyzing outlier in coherently evolutionary relationship between changes of shareholders' owner-ratio and stock price return.In Chapter Three,we select constituent stocks and determine their weights with fundamental factors with Decision Trees and Logistic Regression.Fundamental Stock Price Constituent Index(JOYFI300) is created as a guidance to fundamental investment.The indexing portfolio based on this index is testified better on excess return.To Speak in details,firstly,we choose net profit and trading value as two selection measures by using Decision Trees,and then initial samples of constituent stocks can be found out by these two factors.Secondly,the influence of fundamental factors on weights of constituent stocks can be analysed and determined by Logistic Model.Finally,after test and comparison,we find that JOYFI300 Indexing Portfolio is better on excess return than Shanghai-Shenzhen 300 and CITIC/S&P 300 Indexing Portfolio.In Chapter Four,a Contract Selection and Assessment Model is used for the first time to measure the change of overall risk of Stock Index Futures Contract, because most of the conventional models for valuating hedging efficiency of Stock Index Futures Contract are based only on reduction of cash price risk.The investors or exchange can assess risk exposure of hedger according to the change of basis risk, liquidity risk and trading manipulation risk after introducing Stock Index Futures Contract by using this model,and it also intends to provide a reference of decision-making for risk management.In Chapter Five,we firstly decompose original data involved in Singapore Xinhua/FTSE A50 Stock Index Futures Contract on scale-by-scale basis with Maximum Overlap Discrete Wavelet Transformation.Optimal hedge ratio is estimated under different time scales by taking minimum semi-variance as hedge target.In comparison with minimum wavelet variance hedge ratio under each scale, the empirical result indicates that hedge ratio and correlation of the rate of return between futures and spot go higher along with time scale.Taking semi-variance as hedge target can lead to a better excess return on hedge portfolio.The longer the length of time horizon is,the more excellently the excess return performs.Secondly, we improve Minimum Semi-variance Model with fitting the joint probability density function of rate of return between futures and spot prices based on wavelet density estimation method for improving goodness of fit for the first time.In comparison with empirical and kernel function density estimation method,the empirical result indicates that fitted results of wavelet density estimation is better for data with nonlinear correlation structure.Minimum Semi-variance Model based on wavelet density estimation can eliminate downside risk of hedge portfolio more effectively than other methods.This approach also can lead to a better excess return on hedge portfolio than Minimum Variance Model.In Chapter Six,we summarize this dissertation and point out the directions for further research and improvement in the future in terms of the research content and defect.