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Hierarchical structure of Turkey’s foreign trade

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

Abstract

We examine the hierarchical structures of Turkey’s foreign trade by using real prices of their commodity export and import move together over time. We obtain the topological properties among the countries based on Turkey’s foreign trade during the 1996–2010 period by using the concept of hierarchical structure methods (minimal spanning tree, (MST) and hierarchical tree, (HT)). These periods are divided into two subperiods, such as 1996–2002 and 2003–2010, in order to test various time-window and observe the temporal evolution. We perform the bootstrap techniques to investigate a value of the statistical reliability to the links of the MSTs and HTs. We also use a clustering linkage procedure in order to observe the cluster structure much better. From the structural topologies of these trees, we identify different clusters of countries according to their geographical location and economic ties. Our results show that the DE (Germany), UK (United Kingdom), FR (France), IT (Italy) and RU (Russia) are more important within the network, due to a tighter connection with other countries. We have also found that these countries play a significant role for Turkey’s foreign trade and have important implications for the design of portfolio and investment strategies.

Suggested Citation

  • Kantar, Ersin & Deviren, Bayram & Keskin, Mustafa, 2011. "Hierarchical structure of Turkey’s foreign trade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3454-3476.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:20:p:3454-3476
    DOI: 10.1016/j.physa.2011.05.004
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    Citations

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    Cited by:

    1. Kantar, Ersin & Keskin, Mustafa, 2013. "The relationships between electricity consumption and GDP in Asian countries, using hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5678-5684.
    2. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    3. Paulus, Michal & Kristoufek, Ladislav, 2015. "Worldwide clustering of the corruption perception," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 351-358.
    4. Ladislav Kristoufek & Karel Janda & David Zilberman, 2013. "Regime-dependent topological properties of biofuels networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(2), pages 1-12, February.
    5. Samitas, Aristeidis & Kampouris, Elias & Polyzos, Stathis, 2022. "Covid-19 pandemic and spillover effects in stock markets: A financial network approach," International Review of Financial Analysis, Elsevier, vol. 80(C).
    6. Araújo, Tanya & Faustino, Rui, 2017. "The topology of inter-industry relations from the Portuguese national accounts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 236-248.
    7. 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.
    8. Kristoufek, Ladislav & Janda, Karel & Zilberman, David, 2012. "Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective," Energy Economics, Elsevier, vol. 34(5), pages 1380-1391.
    9. Haishu Qiao & Yue Xia & Ying Li, 2016. "Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-25, June.
    10. Akgüller, Ömer & Balcı, Mehmet Ali, 2018. "Geodetic convex boundary curvatures of the communities in stock market networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 569-581.
    11. 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.
    12. Haiming Long & Ji Zhang & Nengyu Tang, 2017. "Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    13. Kristoufek, Ladislav & Janda, Karel & Zilberman, David, 2012. "Relationship Between Prices of Food, Fuel and Biofuel," 131st Seminar, September 18-19, 2012, Prague, Czech Republic 135793, European Association of Agricultural Economists.
    14. Deviren, Seyma Akkaya & Deviren, Bayram, 2016. "The relationship between carbon dioxide emission and economic growth: Hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 429-439.
    15. Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).

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