Please wait a minute...
文章检索
复杂系统与复杂性科学  2018, Vol. 15 Issue (4): 50-59    DOI: 10.13306/j.1672-3813.2018.04.007
  本期目录 | 过刊浏览 | 高级检索 |
有色金属国际期货市场价格联动效应演化分析——以铜、铝、锌为例
董晓娟, 安海岗, 董志良
河北地质大学管理科学与工程学院,石家庄 050031
Evolution Analysis of Price Linkage Effect in the International Futures Market of Non-Ferrous Metals:Case of Copper, Aluminum and Zinc
DONG Xiaojuan, AN Haigang, DONG Zhiliang
School of Management Science and Engineering, Hebei GEO University, Shijiazhuang 050031, China
全文: PDF(1982 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 近几年来,有色金属期货市场价格波动较大,尤其是对于交易较频繁的铜、铝、锌,交易风险不断加大。本文研究了有色金属期货价格联动关系的动力学特征,以铜、铝、锌为例基于回归分析构建了铜铝锌期货价格联动关系的两个有向加权网络,分析了价格网络中度分布、边权分布、中介中心度、接近中心度等网络拓扑结构及其演化特征。结果表明,2008~2018年铜铝锌期货价格联动关系较稳定于少数关键关系模式。通过对网络中边权进行分析,发现铜铝锌期货价格联动关系模式在一段时间内趋向于保持稳定。本文通过对网络的中介中心度和接近中心度进行分析,发现关键媒介节点的频繁出现有一定规律性,并且与市场价格趋势以及3种金属的联动关系特征有直接关系。因此,基于以上结果,本文提出了识别价格联动效应变化趋势的方法,并对该方法运用于投资提出了建议。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
董晓娟
安海岗
董志良
董晓娟
安海岗
董志良
关键词 有色金属时间序列复杂网络价格波动相关效应回归分析    
Abstract:In recent years, the price of non-ferrous metal futures market has fluctuated greatly and the trading risk has been increasing. In this paper, the dynamic characteristics of the linkage relationship of non-ferrous metal futures prices are studied. Based on the analysis of the prices of copper, aluminum and zinc, the paper constructed two directed weighted networks of copper-aluminum-zinc futures price linkage relationship. The paper analyzed the moderate distribution and marginal power of price networks. Then the paper studied the network topology and its evolution characteristics such as distribution of median centrality, and proximity. The results show that the price linkage of copper, aluminum and zinc futures in 2008-2018 is relatively stable in a few key relationship models. Through the analysis of the marginal rights in the network, it is found that the copper-aluminum-zinc futures price linkage relation model tends to remain stable for a period of time. By analyzing the betweenness centrality and the closeness centrality of the network, itis found that the frequent occurrence of key media nodes has certain regularity, and it is directly related to the market price trend and the linkage characteristics of the three metals. Therefore, based on the above results, this paper proposes a method to identify the trend of price linkage effect, and puts forward effective suggestions for the application of this method.
Key wordsnon-ferrous metals    time series    complex networks    price fluctuation relation effects    regression analysis
     出版日期: 2019-05-16
:  F831  
基金资助:国家社会科学基金(17BGL202);河北省人力资源与社会保障厅项目(JRSHZ201803011);校内预研项目(KY201601)
作者简介: 董晓娟(1980),女,河北赞皇人,硕士研究生,讲师,主要研究方向为复杂网络、电子商务。
引用本文:   
董晓娟, 安海岗, 董志良. 有色金属国际期货市场价格联动效应演化分析——以铜、铝、锌为例[J]. 复杂系统与复杂性科学, 2018, 15(4): 50-59.
DONG Xiaojuan, AN Haigang, DONG Zhiliang. Evolution Analysis of Price Linkage Effect in the International Futures Market of Non-Ferrous Metals:Case of Copper, Aluminum and Zinc[J]. Complex Systems and Complexity Science, 2018, 15(4): 50-59.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2018.04.007      或      https://fzkx.qdu.edu.cn/CN/Y2018/V15/I4/50
[1]Martino S, Parson L M. Spillovers between cobalt, copper and nickel prices: implications for deep seabed mining[J]. Mineral Economics, 2013, 25(2/3):107127.
[2]Hansson M, Andersson O, Holmberg O. ARMA and GARCH models for silver, nickel and copper price returns[D]. Mathematics & Statistics, 2015.
[3]Long R, Wang L. Research on the dynamic relationship among China’s metal futures, spot price and London′s futures price[J]. International Journal of Business & Management, 2009, 3(5):5056.
[4]Todorova N, Worthington A, Souek M. Realized volatility spillovers in the non-ferrous metal futures market[J]. Resources Policy, 2014, 39(39):2131.
[5]Chen J, Zhu X, Zhong M. Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: a Markov-switching VAR analysis[J]. Resources Policy, 2018,doi:10.1016/j.resourpol.2018.04.015.
[6]Otto S. Does the London metal exchange follow a random walk? evidence from the predictability of futures prices[J]. Febs Letters, 2010, 47(2):295298.
[7]吴丹, 胡振华. 中国有色金属期货收益率之间的相互关系研究[J]. 求索, 2016(11):8993.
Wu Dan, Hu Zhenhua. The relationship between the yield of Chinese nonferrous metal futures [J]. Seeking, 2016 (11):8993.
[8]金吾伦, 郭元林. 复杂性科学及其演变[J]. 复杂系统与复杂性科学, 2004, 1(1):15.
Jin Wulun, Guo Yuanlin. Complexity science and its evolution [J]. Complex Systems and Complexity Science, 2004, 1(1):15.
[9]宋学锋. 复杂性科学研究现状与展望[J]. 复杂系统与复杂性科学, 2005, 2(1):1017.
Song Xuefeng. The present situation and prospect of complexity science[J].Complex Systems and Complexity Science,2005,2(1):1017.
[10] 蔡世民, 洪磊, 傅忠谦,等. 基于复杂网络的金融市场网络结构实证研究[J]. 复杂系统与复杂性科学, 2011, 8(3):2933.
[11] Zhang J, Sun J, Luo X, et al. Characterizing pseudoperiodic time series through the complex network approach[J]. Physica D, 2008,237(22), 28562865.
[12] Zhang J, Small M. Complex network from pseudoperiodic time series: topology versus dynamics[J]. Phys Rev Lett, 2006,96(23): 238701.
[13] Lacasa L, Luque B, Ballesteros F, et al. From time series to complex networks:the visibility graph[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008,105(13): 49724975.
[14] Marwan N, Donges J F, Zou Y, et al. Complex network approach for recurrence analysis of time series[J]. Physicis Letter A, 2009,373(46): 42464254.
[15] An H, Gao X, Fang W, et al. Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: a complex network approach[J]. Applied Energy, 2014, 136(1):10671075.
[16] Gao X, An H, Fang W, et al. Transmission of linear regression patterns between time series: from relationship in time series to complex networks.[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2014, 90(1):012818.
[17] 王楠. 期货时间序列复杂网络特征与投资组合策略研究[D]. 北京:中国地质大学, 2016.
Wang Nan. Research on complex network characteristics and investment portfolio strategy of futures time series [D]. Beijing: China University of Geosciences, 2016.
[18] 赵小文. 基于时间序列数据的复杂网络重构[D]. 西安:西安电子科技大学, 2017.
Zhao Xiaowen. Complex network reconstruction based on time series data [D]. Xi′an: Xi′an University of Electronic Science and Technology, 2017.
[19] 郭建民. 时间序列复杂网络建网方法的性能分析及应用研究[D]. 天津:天津大学, 2016.
Guo Jianmin. Performance analysis and application of time series complex network construction method [D]. Tianjin: Tianjin University, 2016.
[1] 聂廷远, 王艳伟, 聂晶晶, 刘鹏飞. 基于注意力机制和复杂网络的FPGA可布性预测[J]. 复杂系统与复杂性科学, 2026, 23(1): 53-59.
[2] 户佐安, 杨江浩, 邓锦程. 考虑多元变量的世界航空网络综合鲁棒性研究[J]. 复杂系统与复杂性科学, 2026, 23(1): 60-69.
[3] 孙小慧, 刘毅, 米玉梅, 吕凯. 韧性视角下城市地铁与常规公交网络关键站点及线路识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 26-36.
[4] 牟奇锋, 李晓倩. 基于邻接矩阵的复杂网络演化融合迭代方法[J]. 复杂系统与复杂性科学, 2026, 23(1): 79-86.
[5] 孙文静, 余路粉, 潘文林, 蓝春江. 基于节点影响因子和贡献因子的复杂网络重要节点识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 87-95.
[6] 卢新彪, 刘泽诚, 陈贵允, 杨铁流, 高兴. 基于图卷积网络的复杂网络能控性提升方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 24-28.
[7] 周青, 李依函, 陈文冲. “互联网+”企业创新生态系统网络演化分析[J]. 复杂系统与复杂性科学, 2025, 22(4): 1-7.
[8] 章浩淳, 寇博潇, 张泰杰, 唐智慧. 基于Granger Causality的滑坡机理网络客观权值确定方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 63-70.
[9] 韩世翔, 闫光辉, 裴华艳. 复杂网络上双向免疫对传染病传播的影响[J]. 复杂系统与复杂性科学, 2025, 22(4): 55-62.
[10] 张琦, 汪小帆. 复杂网络观点动力学分析与干预若干研究进展[J]. 复杂系统与复杂性科学, 2025, 22(2): 31-44.
[11] 张明磊, 宋玉蓉, 曲鸿博. 基于图注意力机制的复杂网络关键节点识别[J]. 复杂系统与复杂性科学, 2025, 22(2): 113-119.
[12] 陶昭, 侯忠生. 复杂网络的无模型自适应牵制控制[J]. 复杂系统与复杂性科学, 2025, 22(2): 120-127.
[13] 李伟莎, 王淑良, 宋博. 基于强化学习风电并网策略下的韧性分析[J]. 复杂系统与复杂性科学, 2025, 22(2): 128-134.
[14] 张耀波, 张胜, 王雨萱, 熊聪源. 基于K-shell的复杂网络簇生长维数研究[J]. 复杂系统与复杂性科学, 2025, 22(1): 11-17.
[15] 詹秀秀, 叶涛, 刘闯, 刘雪梅. 农产品贸易网络中国家影响力分析与研究[J]. 复杂系统与复杂性科学, 2025, 22(1): 26-32.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed