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Hierarchical structure of the European countries based on debts as a percentage of GDP during the 2000–2011 period

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  • Kantar, Ersin
  • Deviren, Bayram
  • Keskin, Mustafa

Abstract

We investigate hierarchical structures of the European countries by using debt as a percentage of Gross Domestic Product (GDP) of the countries as they change over a certain period of time. We obtain the topological properties among the countries based on debt as a percentage of GDP of European countries over the period 2000–2011 by using the concept of hierarchical structure methods (minimal spanning tree, (MST) and hierarchical tree, (HT)). This period is also divided into two sub-periods related to 2004 enlargement of the European Union, namely 2000–2004 and 2005–2011, in order to test various time-window and observe the temporal evolution. The bootstrap techniques is applied to see a value of statistical reliability of the links of the MSTs and HTs. The clustering linkage procedure is also used to observe the cluster structure more clearly. From the structural topologies of these trees, we identify different clusters of countries according to their level of debts. Our results show that by the debt crisis, the less and most affected Eurozone’s economies are formed as a cluster with each other in the MSTs and hierarchical trees.

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  • Kantar, Ersin & Deviren, Bayram & Keskin, Mustafa, 2014. "Hierarchical structure of the European countries based on debts as a percentage of GDP during the 2000–2011 period," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 95-107.
    • Handle: RePEc:eee:phsmap:v:414:y:2014:i:c:p:95-107
    DOI: 10.1016/j.physa.2014.07.001
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    References listed on IDEAS

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    1. Jamal Ibrahim Haidar, 2012. "Sovereign Credit Risk in the Eurozone," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 13(1), pages 123-136, January.
    2. Chiarella, Carl & He, Xue-Zhong & Hommes, Cars, 2006. "Moving average rules as a source of market instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 12-17.
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    Cited by:

    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Yao, Can-Zhong & Lin, Ji-Nan & Lin, Qing-Wen & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2016. "A study of causality structure and dynamics in industrial electricity consumption based on Granger network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 297-320.
    3. Kantar, Ersin & Aslan, Alper & Deviren, Bayram & Keskin, Mustafa, 2016. "Hierarchical structure of the countries based on electricity consumption and economic growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 1-10.
    4. Dias, João, 2017. "Unemployment and sovereign debt crisis in the Eurozone: A k-means-r analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 108-117.
    5. Matesanz, David & Ortega, Guillermo J., 2015. "Sovereign public debt crisis in Europe. A network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 756-766.
    6. Yao, Can-Zhong & Lin, Ji-Nan & Liu, Xiao-Feng, 2016. "A study of hierarchical structure on South China industrial electricity-consumption correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 129-145.
    7. Hu, Fei & Zhao, Shangmei & Bing, Tao & Chang, Yiming, 2017. "Hierarchy in industrial structure: The cases of China and the USA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 871-882.
    8. Zeitsch, Peter J., 2019. "A jump model for credit default swaps with hierarchical clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 737-775.

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