IDEAS home Printed from https://ideas.repec.org/a/spr/jeicoo/v14y2019i4d10.1007_s11403-018-0231-x.html
   My bibliography  Save this article

Modeling the impulse response complex network for studying the fluctuation transmission of price indices

Author

Listed:
  • Qingru Sun

    (China University of Geosciences
    Ministry of Natural Resources)

  • Xiangyun Gao

    (China University of Geosciences
    Ministry of Natural Resources)

  • Shaobo Wen

    (China University of Geosciences
    Ministry of Natural Resources)

  • Sida Feng

    (China University of Geosciences
    Ministry of Natural Resources)

  • Ze Wang

    (China University of Geosciences
    Ministry of Natural Resources)

Abstract

We provide a method for analyzing the transmission of fluctuation among price indices, which combines the complex network method and the impulse response function (IRF). The transmission relationships of price fluctuation among numerous price indices are remarkably complicated, especially under external shocks. In this paper, the empirical data of US, China and Japan are selected as samples, and we analyze the transmission relationships between each two price indices using IRF and construct directed positive and negative impulse response networks of US, China and Japan, respectively. We analyze and compare the price fluctuation transmission characteristics of different countries under external shocks from the aspect of the transmission range, degree, intermediary, clustering and time. The results of the analysis indicate that (1) Positive networks are tighter than negative networks; (2) There is positive correlation between the range and degree of price fluctuation transmission; (3) The intermediation ability of price fluctuation transmission is high; (4) The clustering of networks is different and the transmission probability among clusters are different; (5) The transmission speed of positive networks is faster than that of negative networks. This research could provide some implications for investors in relation to decentralizing investments.

Suggested Citation

  • Qingru Sun & Xiangyun Gao & Shaobo Wen & Sida Feng & Ze Wang, 2019. "Modeling the impulse response complex network for studying the fluctuation transmission of price indices," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 835-858, December.
    • Handle: RePEc:spr:jeicoo:v:14:y:2019:i:4:d:10.1007_s11403-018-0231-x
    DOI: 10.1007/s11403-018-0231-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11403-018-0231-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11403-018-0231-x?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. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Dynamic Spillovers of Oil Price Shocks and Policy Uncertainty," Department of Economics Working Paper Series 166, WU Vienna University of Economics and Business.
    2. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2017. "Microeconomic Origins of Macroeconomic Tail Risks," American Economic Review, American Economic Association, vol. 107(1), pages 54-108, January.
    3. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 23-48, Fall.
    4. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    5. Matthew O. Jackson, 2014. "Networks in the Understanding of Economic Behaviors," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 3-22, Fall.
    6. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2013. "Null models of economic networks: the case of the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 75-107, April.
    7. Rajibur Reza & Gurudeo Anand Tularam & Bin Li, 2017. "An investigation into the interdependence of global water indices: a VAR analysis," Applied Economics, Taylor & Francis Journals, vol. 49(8), pages 769-796, February.
    8. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Working Papers 793, Barcelona School of Economics.
    9. Daron Acemoglu & Ufuk Akcigit & William Kerr, 2016. "Networks and the Macroeconomy: An Empirical Exploration," NBER Macroeconomics Annual, University of Chicago Press, vol. 30(1), pages 273-335.
    10. Ya-Chun Gao & Yong Zeng & Shi-Min Cai, 2015. "Influence network in Chinese stock market," Papers 1503.00823, arXiv.org.
    11. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 168-185.
    12. 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.
    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. Xavier Gabaix, 2011. "The Granular Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 79(3), pages 733-772, May.
    15. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    16. Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Tsionas, Efthymios G. & Minou, Chrysanthi, 2015. "System estimation of GVAR with two dominants and network theory: Evidence for BRICs," Economic Modelling, Elsevier, vol. 51(C), pages 604-616.
    17. Theodore Tsekeris, 2017. "Network analysis of inter-sectoral relationships and key sectors in the Greek economy," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 413-435, July.
    18. Fernandez, Viviana, 2014. "Linear and non-linear causality between price indices and commodity prices," Resources Policy, Elsevier, vol. 41(C), pages 40-51.
    19. Tiwari, Aviral Kumar & Mutascu, Mihai & Andries, Alin Marius, 2013. "Decomposing time-frequency relationship between producer price and consumer price indices in Romania through wavelet analysis," Economic Modelling, Elsevier, vol. 31(C), pages 151-159.
    20. Balke, Nathan S. & Wynne, Mark A., 2007. "The relative price effects of monetary shocks," Journal of Macroeconomics, Elsevier, vol. 29(1), pages 19-36, March.
    21. Marco Bardoscia & Giacomo Livan & Matteo Marsili, 2015. "Statistical mechanics of complex economies," Papers 1511.09203, arXiv.org, revised Apr 2017.
    22. Xiangyun Gao & Haizhong An & Weiqiong Zhong, 2013. "Features of the Correlation Structure of Price Indices," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.
    23. Sharma, Susan Sunila, 2016. "Can consumer price index predict gold price returns?," Economic Modelling, Elsevier, vol. 55(C), pages 269-278.
    24. Lahmiri, Salim, 2017. "Cointegration and causal linkages in fertilizer markets across different regimes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 181-189.
    25. Xiaopeng Guo & Jiaxing Shi & Dongfang Ren, 2016. "Coal Price Forecasting and Structural Analysis in China," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-7, October.
    26. 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.
    27. Lenzu, Simone & Tedeschi, Gabriele, 2012. "Systemic risk on different interbank network topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4331-4341.
    28. Tu, Chengyi, 2014. "Cointegration-based financial networks study in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 245-254.
    29. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Dynamic spillovers of oil price shocks and economic policy uncertainty," Energy Economics, Elsevier, vol. 44(C), pages 433-447.
    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. 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).

    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. 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.
    2. 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.
    3. Ernesto Pasten & Raphael S. Schoenle & Michael Weber & Michael Weber, 2017. "Price Rigidities and the Granular Origins of Aggregate Fluctuations," CESifo Working Paper Series 6619, CESifo.
    4. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    5. Ernesto Pasten & Raphael Schoenle & Michael Weber, 2017. "Price Rigidity and the Origins of Aggregate Fluctuations," NBER Working Papers 23750, National Bureau of Economic Research, Inc.
    6. repec:zbw:bofrdp:2018_003 is not listed on IDEAS
    7. Pasten, Ernesto & Schoenle, Raphael & Weber, Michael, 2017. "Price rigidities and the granular origins of aggregate fluctuations," Working Paper Series 2102, European Central Bank.
    8. Dong, Feng & Wen, Yi, 2019. "Long and Plosser meet Bewley and Lucas," Journal of Monetary Economics, Elsevier, vol. 102(C), pages 70-92.
    9. Lee, Dongyeol, 2021. "Propagation of economic shocks through vertical and trade linkages in Korea: An empirical analysis," Japan and the World Economy, Elsevier, vol. 60(C).
    10. Dongyeol Lee, 2019. "Transmission of Domestic and External Shocks through Input-Output Network: Evidence from Korean Industries," IMF Working Papers 2019/117, International Monetary Fund.
    11. Raffaele Giammetti & Alberto Russo & Mauro Gallegati, 2020. "Key sectors in input–output production networks: An application to Brexit," The World Economy, Wiley Blackwell, vol. 43(4), pages 840-870, April.
    12. Dongyeol Lee, 2019. "Trade Linkages and International Business Cycle Comovement: Evidence from Korean Industry Data," IMF Working Papers 2019/116, International Monetary Fund.
    13. Peydró, José-Luis & Jiménez, Gabriel & Kenan, Huremovic & Moral-Benito, Enrique & Vega-Redondo, Fernando, 2020. "Production and financial networks in interplay: Crisis evidence from supplier-customer and credit registers," CEPR Discussion Papers 15277, C.E.P.R. Discussion Papers.
    14. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    15. Erik Frohm & Vanessa Gunnella, 2021. "Spillovers in global production networks," Review of International Economics, Wiley Blackwell, vol. 29(3), pages 663-680, August.
    16. Mundt, Philipp, 2021. "The formation of input–output architecture: Evidence from the European Union," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 89-104.
    17. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna & Luu, Duc Thi, 2022. "The multilayer architecture of the global input-output network and its properties," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 304-341.
    18. João Amador & Sónia Cabral, 2017. "Networks of Value-added Trade," The World Economy, Wiley Blackwell, vol. 40(7), pages 1291-1313, July.
    19. Frohm, Erik & Gunnella, Vanessa, 2017. "Sectoral interlinkages in global value chains: spillovers and network effects," Working Paper Series 2064, European Central Bank.
    20. Jennifer La'O & Alireza Tahbaz‐Salehi, 2022. "Optimal Monetary Policy in Production Networks," Econometrica, Econometric Society, vol. 90(3), pages 1295-1336, May.
    21. Glenn Magerman & Karolien De Bruyne & Emmanuel Dhyne & Jan Van Hove, 2016. "Heterogeneous Firms and the Micro Origins of Aggregate Fluctuations," Working Papers ECARES ECARES 2016-35, ULB -- Universite Libre de Bruxelles.

    More about this item

    Keywords

    Econophysics; Complex network; Impulse response function; Fluctuation transmission; Price index;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

    Statistics

    Access and download statistics

    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:spr:jeicoo:v:14:y:2019:i:4:d:10.1007_s11403-018-0231-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.