IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v473y2017icp188-204.html
   My bibliography  Save this article

Systemic risk and spatiotemporal dynamics of the consumer market of China

Author

Listed:
  • Wang, Minggang
  • Tian, Lixin
  • Xu, Hua
  • Li, Weiyu
  • Du, Ruijin
  • Dong, Gaogao
  • Wang, Jie
  • Gu, Jiani

Abstract

The consumer price index (CPI) contains rich information of the consumer market, in order to characterize the essential characteristics of the consumer market of China, a novel method by using complex network theory is proposed to visualizing the evolution and transformation characteristics of correlated modes among the regional consumer markets. CPI data of 31 provinces and cities of China are selected as sample data. Underlying dynamics of time-evolving correlation networks are revealed. A formula to measure the systemic risk in the consumer market is designed. And the correlation modes transmission network of the regional consumer markets is obtained. Numerical simulations show that the consumer market network has co-movement, group-occurring and small-word property. Different regions played different roles in the consumer market of China. The risk in the consumer market presented a decreasing trend from April 2013 but remain at the high level. Different from the stochastic system, the consumer market of China both has the short-range correlation and the long-range correlation. The strength of correlation modes transmission network basically satisfies a power-law distribution. The correlation modes are transferred into each other conveniently, although the consumer market system is highly complicated. The transformation of the correlation patterns of the regional consumer markets mainly revolves around three core correlation modes and each transformation needs to undergo 4 non-core modes.

Suggested Citation

  • Wang, Minggang & Tian, Lixin & Xu, Hua & Li, Weiyu & Du, Ruijin & Dong, Gaogao & Wang, Jie & Gu, Jiani, 2017. "Systemic risk and spatiotemporal dynamics of the consumer market of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 188-204.
    • Handle: RePEc:eee:phsmap:v:473:y:2017:i:c:p:188-204
    DOI: 10.1016/j.physa.2017.01.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117300328
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.01.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. M. E. J. Newman & D. J. Watts, 1999. "Renormalization Group Analysis of the Small-World Network Model," Working Papers 99-04-029, Santa Fe Institute.
    2. Randal Verbrugge, 2012. "Do the Consumer Price Index's Utilities Adjustments for Owners’ Equivalent Rent Distort Inflation Measurement?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 143-148.
    3. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Huang, Xuan, 2016. "Time–frequency featured co-movement between the stock and prices of crude oil and gold," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 985-995.
    4. An, Haizhong & Gao, Xiangyun & Fang, Wei & Ding, Yinghui & Zhong, Weiqiong, 2014. "Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: A complex network approach," Applied Energy, Elsevier, vol. 136(C), pages 1067-1075.
    5. Dong, Yan & Huang, Wenwen & Liu, Zonghua & Guan, Shuguang, 2013. "Network analysis of time series under the constraint of fixed nearest neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 967-973.
    6. Tiwari, Aviral Kumar & Suresh K.G., & Arouri, Mohamed & Teulon, Frédéric, 2014. "Causality between consumer price and producer price: Evidence from Mexico," Economic Modelling, Elsevier, vol. 36(C), pages 432-440.
    7. Shahbaz, Muhammad & Tiwari, Aviral Kumar & Tahir, Mohammad Iqbal, 2012. "Does CPI Granger-cause WPI? New extensions from frequency domain approach in Pakistan," Economic Modelling, Elsevier, vol. 29(5), pages 1592-1597.
    8. Aviral Kumar Tiwari & Muhammad Shahbaz, 2013. "Modelling the Relationship between Whole Sale Price and Consumer Price Indices: Cointegration and Causality Analysis for India," Global Business Review, International Management Institute, vol. 14(3), pages 397-411, September.
    9. Paul Armknecht & Mick Silver, 2014. "Post-Laspeyres: The Case for a New Formula for Compiling Consumer Price Indexes," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(2), pages 225-244, June.
    10. Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.
    11. Dipankor Coondoo & Amita Majumder & Somnath Chattopadhyay, 2011. "Estimating Spatial Consumer Price Indices Through Engel Curve Analysis," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 57(1), pages 138-155, March.
    12. Wang, Minggang & Tian, Lixin, 2016. "From time series to complex networks: The phase space coarse graining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 456-468.
    13. Tiwari, Aviral Kumar, 2012. "An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain," Economic Modelling, Elsevier, vol. 29(5), pages 1571-1578.
    14. Wang, Minggang & Tian, Lixin & Du, Ruijin, 2016. "Research on the interaction patterns among the global crude oil import dependency countries: A complex network approach," Applied Energy, Elsevier, vol. 180(C), pages 779-791.
    15. Gao, Xiangyun & An, Haizhong & Fang, Wei & Li, Huajiao & Sun, Xiaoqi, 2014. "The transmission of fluctuant patterns of the forex burden based on international crude oil prices," Energy, Elsevier, vol. 73(C), pages 380-386.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Ze & Gao, Xiangyun & Tang, Renwu & Liu, Xueyong & Sun, Qingru & Chen, Zhihua, 2019. "Identifying influential nodes based on fluctuation conduction network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 355-369.
    2. Guangyong Zhang & Lixin Tian & Wenbin Zhang & Xu Yan & Bingyue Wan & Zaili Zhen, 2020. "A Study on the Similarities and Differences of the Conventional Gasoline Spot Price Fluctuation Network between Different Harbors," Sustainability, MDPI, vol. 12(2), pages 1-25, January.
    3. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    5. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
    6. Si, Jingjian & Zhou, Jinsheng & Gao, Xiangyun & Ze, Wang & Tao, Wu & Zhao, Yiran, 2022. "Reconstructing a complex financial network using compressed sensing based on low-frequency time series data," Finance Research Letters, Elsevier, vol. 49(C).
    7. Zhen Zhang & Minggang Wang & Lixin Tian & Wenbin Zhang, 2017. "Research on the development efficiency of regional high-end talent in China: A complex network approach," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-18, December.
    8. Huan Chen & Lixin Tian & Minggang Wang & Zaili Zhen, 2017. "Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks," Sustainability, MDPI, vol. 9(4), pages 1-29, April.
    9. Chen, Lin & Han, Qian & Qiao, Zhilin & Stanley, H. Eugene, 2020. "Correlation analysis and systemic risk measurement of regional, financial and global stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    10. Wang, Minggang & Xu, Hua & Tian, Lixin & Eugene Stanley, H., 2018. "Degree distributions and motif profiles of limited penetrable horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 620-634.
    11. Sun, Qingru & Gao, Xiangyun & Wen, Shaobo & Chen, Zhihua & Hao, Xiaoqing, 2018. "The transmission of fluctuation among price indices based on Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 36-49.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Minggang & Tian, Lixin & Du, Ruijin, 2016. "Research on the interaction patterns among the global crude oil import dependency countries: A complex network approach," Applied Energy, Elsevier, vol. 180(C), pages 779-791.
    2. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    3. Wang, Minggang & Xu, Hua & Tian, Lixin & Eugene Stanley, H., 2018. "Degree distributions and motif profiles of limited penetrable horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 620-634.
    4. Huan Chen & Lixin Tian & Minggang Wang & Zaili Zhen, 2017. "Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks," Sustainability, MDPI, vol. 9(4), pages 1-29, April.
    5. Guangyong Zhang & Lixin Tian & Wenbin Zhang & Xu Yan & Bingyue Wan & Zaili Zhen, 2020. "A Study on the Similarities and Differences of the Conventional Gasoline Spot Price Fluctuation Network between Different Harbors," Sustainability, MDPI, vol. 12(2), pages 1-25, January.
    6. An, Sufang & Gao, Xiangyun & Jiang, Meihui & Sun, Xiaoqi, 2018. "Multivariate financial time series in the light of complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1241-1255.
    7. Sun, Qingru & Gao, Xiangyun & Wen, Shaobo & Chen, Zhihua & Hao, Xiaoqing, 2018. "The transmission of fluctuation among price indices based on Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 36-49.
    8. Qingru Sun & Xiangyun Gao & Ze Wang & Siyao Liu & Sui Guo & Yang Li, 2020. "Quantifying the risk of price fluctuations based on weighted Granger causality networks of consumer price indices: evidence from G7 countries," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 821-844, October.
    9. Chen, Weidong & Xiong, Shi & Chen, Quanyu, 2022. "Characterizing the dynamic evolutionary behavior of multivariate price movement fluctuation in the carbon-fuel energy markets system from complex network perspective," Energy, Elsevier, vol. 239(PA).
    10. Xiao, Jiang & Wang, Minggang & Tian, Lixin & Zhen, Zaili, 2018. "The measurement of China’s consumer market development based on CPI data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 664-680.
    11. Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.
    12. An, Sufang & Gao, Xiangyun & An, Haizhong & An, Feng & Sun, Qingru & Liu, Siyao, 2020. "Windowed volatility spillover effects among crude oil prices," Energy, Elsevier, vol. 200(C).
    13. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    14. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
    15. Gao, Xiangyun & Fang, Wei & An, Feng & Wang, Yue, 2017. "Detecting method for crude oil price fluctuation mechanism under different periodic time series," Applied Energy, Elsevier, vol. 192(C), pages 201-212.
    16. Ülke, Volkan & Ergun, Ugur, 2013. "The Relationship between Consumer Price and Producer Price Indices in Turkey," MPRA Paper 59437, University Library of Munich, Germany.
    17. Li, Panpan & Dong, Zhiliang, 2020. "Time-varying network analysis of fluctuations between crude oil and Chinese and U.S. gold prices in different periods," Resources Policy, Elsevier, vol. 68(C).
    18. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2017. "A novel weight determination method for time series data aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 42-55.
    19. Zhen Zhang & Minggang Wang & Lixin Tian & Wenbin Zhang, 2017. "Research on the development efficiency of regional high-end talent in China: A complex network approach," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-18, December.
    20. Yao, Can-Zhong & Lin, Ji-Nan & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2015. "The study of RMB exchange rate complex networks based on fluctuation mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 359-376.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:473:y:2017:i:c:p:188-204. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.