基于最小方差的铝期货套期保值模型研究

发布时间：2018-01-03 03:01

本文关键词：基于最小方差的铝期货套期保值模型研究　出处：《中南大学》2013年硕士论文　论文类型：学位论文

【摘要】：期货市场的一个很重要功能就是规避现货价格大幅度变动所带来的风险,实现这一功能关键是依靠期货的套期保值。在实施套期保值时最重要的就是套期保值比率的确定,然而实际上套期保值比率是很难控制的,因此我们有必要选取恰当的计算套期保值率的模型以使风险达到最低,来获得最佳套期保值的结果。本文基于套期保值组合方差最小化原则,以铝现货和期货的日价格数据,利用OLS模型、B-VAR模型、B-VECM和ECM-GARCH模型分别计算了最佳套期保值比率。在分析中,对期现货价格和收益率序列进行了平稳性检验,并对OLS回归的残差进行了正态分布检验、异方差检验、自相关性检验和自回归条件异方差性检验。以往许多学者都认为静态多期的回归效果优于单期回归,动态模型优于静态。但是本论文对每个模型计算出的套保效果进行了对比解析,发现OLS回归模型计算出的套期保值比率不管在估计区间还是检验区间,它的套保绩效都是较好的。因此我们认为并不是使用越复杂的计量方法就越好,而是要根据实际情况,选择更合适更易于交易者操作的模型。本文得到的结论是：企业应选择OLS模型来计算套保比率。本文图9幅,表29个,参考文献63篇。
[Abstract]:A very important function of futures market is to avoid the risk brought by the sharp change of spot price. The key to achieve this function is to rely on futures hedging. The most important thing in the implementation of hedging is to determine the hedge ratio, but in fact, it is difficult to control the hedge ratio. Therefore, it is necessary to select the appropriate model to calculate the hedging rate in order to minimize the risk and obtain the best hedging results. Based on the principle of minimizing variance of hedging portfolio, this paper uses the daily price data of aluminum spot and futures, using OLS model and B-VAR model. B-VECM and ECM-GARCH models calculate the best hedging ratio respectively. The residuals of OLS regression are tested by normal distribution test, heteroscedasticity test, autocorrelation test and autoregressive conditional heteroscedasticity test. In the past, many scholars think that the static multi-period regression is better than the single-period regression, and the dynamic model is better than the static. It is found that the hedge ratio calculated by the OLS regression model is better in both the estimated interval and the test interval, so we do not think that the more complex the measurement method is, the better the hedging performance is. The conclusion of this paper is that enterprises should choose OLS model to calculate the hedging ratio. 63 references.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F724.5;F224

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