Abstract: The study of Chinese rebar futures market price discovery plays an important role in improving competitiveness of iron and steel industry, striving for the pricing power of iron and steel products, and guiding the development of the rebar futures market. In order to measure the price discovery ability of futures market, this paper creates a permanent transitory common factor model (PT model) and an information share common factor model (IS model) by introducing the least squares algorithm of eliminating residual correlation into the Vector Error Correction Model (VEC). Thus, the right time to make up the existing VEC model cannot effectively reflect the difference between PT common factor and IS common factor, because the residual sequence of the spot market has the larger correlation with the residual sequence of the futures market. And then, the proposed model has been verified by utilizing the data of 933 Chinese rebar futures prices and spot prices from January 4, 2011 to November 28, 2014. The empirical results are as follows. Firstly, in the long term, the weight of rebar spot market common factors is equal to 67.35%, the weight of rebar futures market common factors is equal to 32.65%. It means that the rebar spot market has more rapid, effective and sufficient reaction than the rebar futures market when there is a permanent impact new information on the market. The rebar spot market plays a leading role in price discovery. Secondly, the relative contribution of rebar futures markets for new information on the common factors of variance is equal to 60.34%. It is greater than the relative contribution of the rebar spot market. It indicates that the rebar futures market plays a leading role in price discovery when there is a instantaneous impact new information on the market.

Key words: rebar futures market, price finding, permanent transitory common factor model (PT model), information share common factor model(IS model)

摘要： 螺纹钢期货价格发现功能研究对我国钢铁行业提高竞争力,争取钢铁成品和铁矿石定价权,引导螺纹钢期货市场健康发展具有重要作用。本文在向量误差修正模型(VEC)中引入剔除残差相关性的最小二乘算法,构建了用于测度期现货市场价格发现功能的永久短暂PT和信息份额IS共同因子模型,弥补了现有VEC模型由于求得的期现货残差序列相关性较大,导致PT和IS模型测算的信息贡献度存在较大差异的不足。在此基础上,利用2011年1月至2014年11月中国螺纹钢期现货市场933个日交易数据,验证了模型的有效性。

关键词: 螺纹钢期货, 价格发现, 永久短暂PT模型, 信息份额IS模型