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我国期货市场价格发现功能——以黄金为例


类型:案例|12000字, 类别:原创|系数:4.7, 附件:开题报告|文献综述

【摘要】

我国期货市场价格发现功能——以黄金为例黄金期货在我国扮演着为股指期货甚至于日后不断衍生的金融期货产品探路的角色,严格意义上来讲是我国第一个金融期货品种。但因发展时间短,我国黄金期货价格所含信息的准确性和价格发现功能的实现程度尚无结论。基于上述原因,本文对中国黄金现货价格与黄金期货价格之间的关联性进行了理论分析与实证分析,以此探究价格发现功能是否具现在中国黄金期货市场。在实证分析中,本文采用上海期货交易所和上海黄金交易所的2017年每天交易日收盘价格的历史数据,进行相关性分析、格兰杰因果关系检验、脉冲响应函数以及方差分解。经实证分析研究结果表明:黄金期货价格和黄金现货价格间长期存在稳定均衡关系,短期存在领先—滞后关系。黄金期货和黄金现货价格相互影响,但现货价格在价格发现功能运作中起主导作用。这也从侧面反映出目前我国黄金期货市场价格发现功能仍存在短板。最后本文针对其所存在不足提出了与之对应的措施。
关键词:格兰杰因果关系检验; 方差分解; VAR模型 
The Prise Discovery Function of China’s Futures Market——Take gold as an Example
 
Abstract:Gold futures play a role in exploring the future of stock index futures and even the derivative of financial futures products in China. Strictly speaking, gold futures are the first financial futures products in China. However, due to the short development time, there is no conclusion on the accuracy of the information contained in the gold futures price and the realization of the price discovery function in China. Based on the above reasons, this paper makes a theoretical and empirical analysis on the relationship between the spot price of gold and the price of gold futures in China, so as to explore whether the function of price discovery has the current Chinese gold futures market. In the empirical analysis, this paper uses the Shanghai Futures Exchange and the Shanghai Gold Exchange in 2017. Historical data of closing price, correlation analysis, Granger causality test, impulse response function and variance decomposition. The results of empirical analysis show that there is a stable equilibrium relationship between gold futures price and gold spot price for a long time and a leading-lag relationship in the short term. Gold futures and gold spot prices affect each other, but spot prices play a leading role in the operation of price discovery function. This also reflects the current gold futures market price discovery function is still short board. Finally, this paper puts forward the corresponding measures in view of its shortcomings.
Key words: Granger Causal Relation Test; Variance decomposition; VAR model

 

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