Skip to main content

Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market

Frontiers of Physics volume 8, pages 451–460 (2013)Cite this article

Abstract

A hierarchical cluster-tendency (HCT) method in analyzing the group structure of networks of the global foreign exchange (FX) market is proposed by combining the advantages of both the minimal spanning tree (MST) and the hierarchical tree (HT). Fifty currencies of the top 50 World GDP in 2010 according to World Bank’s database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be “colored” and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

References and notes

  1. R. N. Mantegna, Hierarchical structure in financial markets, Eur. Phys. J. B, 1999, 11(1): 193

    Article  ADS  Google Scholar 

  2. D. M. Song, Z. Q. Jiang, and W. X. Zhou, Statistical properties of world investment networks, Physica A, 2009, 388(12): 2450

    Article  ADS  Google Scholar 

  3. S. Lee, M. J. Kim, S. Y. Lee, S. Y. Kim, and J. H. Ban, The effect of the subprime crisis on the credit risk in global scale, Physica A, 2013, 392(9): 2060

    Article  ADS  Google Scholar 

  4. S. Ahn, J. Choi, G. Lim, K. Y. Cha, S. Kim, and K. Kim, Identifying the structure of group correlation in the Korean financial market, Physica A, 2011, 390(11): 1991

    Article  ADS  Google Scholar 

  5. S. Y. Lee, D. I. Hwang, M. J. Kim, I. G. Koh, and S. Y. Kim, Cross-correlations in volume space: Differences between buy and sell volumes, Physica A, 2011, 390(5): 837

    Article  ADS  Google Scholar 

  6. W. S. Jung, S. Chae, J. S. Yang, and H. T. Moon, Characteristics of the Korean stock market correlations, Physica A, 2006, 361(1): 263

    Article  ADS  Google Scholar 

  7. A. Garas and P. Argyrakis, Correlation study of the Athens stock exchange, Physica A, 2007, 380: 399

    Article  ADS  Google Scholar 

  8. P. Caraiani, Characterizing emerging European stock markets through complex networks: From local properties to self-similar characteristics, Physica A, 2012, 391(13): 3629

    Article  ADS  Google Scholar 

  9. T. Heimo, G. Tibély, J. Saramäki, K. Kaski, and J. Kertész, Spectral methods and cluster structure in correlation-based networks, Physica A, 2008, 387(23): 5930

    Article  ADS  Google Scholar 

  10. S. Battiston, Inner structure of capital control networks, Physica A, 2004, 338(1–2): 107

    Article  ADS  Google Scholar 

  11. M. A. Djauhari, A robust filter in stock networks analysis, Physica A, 2012, 391(20): 5049

    Article  ADS  Google Scholar 

  12. T. Heimo, J. Saramäki, J. P. Onnela, and K. Kaski, Spectral and network methods in the analysis of correlation matrices of stock returns, Physica A, 2007, 383(1): 147

    Article  ADS  Google Scholar 

  13. G. Tibély, J. P. Onnela, J. Saramäki, K. Kaski, and J. Kertész, Spectrum, intensity and coherence in weighted networks of a financial market, Physica A, 2006, 370(1): 145

    Article  ADS  Google Scholar 

  14. B. M. Tabak, T. R. Serra, and D. O. Cajueiro, Topological properties of stock market networks: The case of Brazil, Physica A, 2010, 389(16): 3240

    Article  ADS  Google Scholar 

  15. G. Bonanno, G. Caldarelli, F. Lillo, S. Micciche, N. Vandewalle, and R. N. Mantegna, Networks of equities in financial markets, Eur. Phys. J. B, 2001, 38: 363

    Article  ADS  Google Scholar 

  16. L. Sandoval Jr, Pruning a minimum spanning tree, Physica A, 2012, 391(8): 2678

    Article  ADS  Google Scholar 

  17. W. J. Ma, C. K. Hu, and R. E. Amritkar, Stochastic dynamical model for stock-stock correlations, Phys. Rev. E, 2004, 70(2): 026101

    Article  ADS  Google Scholar 

  18. J. P. Onnela, A. Chakraborti, K. Kaski, and J. Kertész, Dynamic asset trees and Black Monday, Physica A, 2003, 324(1–2): 247

    Article  MathSciNet  ADS  MATH  Google Scholar 

  19. J. P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, and A. Kanto, Dynamics of market correlations: Taxonomy and portfolio analysis, Phys. Rev. E, 2003, 68(5): 056110

    Article  ADS  Google Scholar 

  20. J. P. Onnela, A. Chakraborti, K. Kaski, J. Kertesz, and A. Kanto, Asset trees and asset graphs in financial markets, Phys. Scr., 2003, T106(1): 48

    Article  Google Scholar 

  21. G. Bonnanno, G. Caldarelli, F. Lillo, and R. N. Mantegna, Topology of correlation-based minimal spanning trees in real and model markets, Phys. Rev. E, 2003, 68(4): 046130

    Article  ADS  Google Scholar 

  22. M. H. Jensen, A. Johansen, F. Petroni, and I. Simonsen, Inverse statistics in the foreign exchange market, Physica A, 2004, 340(4): 678

    Article  ADS  Google Scholar 

  23. M. McDonald, O. Suleman, S. Williams, S. Howison, and N. F. Johnson, Impact of unexpected events, shocking news and rumours on foreign exchange market dynamics, Phys. Rev. E, 2008, 77(4): 046110

    Article  ADS  Google Scholar 

  24. M. McDonald, O. Suleman, S. Williams, S. Howison, and N. F. Johnson, Detecting a currency’s dominance or dependence using foreign exchange network trees, Phys. Rev. E, 2005, 72(4): 046106

    Article  ADS  Google Scholar 

  25. S. Drozdz, J. Kwapien, P. Oswiecimka, and R. Rak, The foreign exchange market: return distributions, multifractality, anomalous multifractality and the Epps effect, New J. Phys., 2010, 12(10): 105003

    Article  ADS  Google Scholar 

  26. W. Jang, J. Lee, and W. Chang, Currency crises and the evolution of foreign exchange market evidence from minimum spanning tree, Physica A, 2011, 390(4): 707

    Article  ADS  Google Scholar 

  27. M. Keskin, B. Deviren, and Y. Kocakaplan, Topology of the correlation networks among major currencies using hierarchical structure methods, Physica A, 2011, 390(4): 719

    Article  ADS  Google Scholar 

  28. G. J. Wang, C. Xie, F. Han, and B. Sun, Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree, Physica A, 2012, 391(16): 4136

    Article  ADS  Google Scholar 

  29. J. Kwapien, S. Gworek, S. Drozdz, and A. Gorski, Analysis of a network structure of the foreign currency exchange market, J. Econ. Interact. Coord, 2009, 4(1): 55

    Article  Google Scholar 

  30. R. N. Mantegna and H. Eugene Stanley, An Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge: Cambridge University Press, 2010

    Google Scholar 

  31. E. Kantar, B. Deviren, and M. Keskin, Hierarchical structure of Turkey’s foreign trade, Physica A, 2011, 390(20): 3454

    Article  ADS  Google Scholar 

  32. Y. Kocakaplan, B. Deviren, and M. Keskin, Hierarchical structures of correlations networks among Turkey’s exports and imports by currencies, Physica A, 2012, 391(24): 6509

    Article  ADS  Google Scholar 

  33. http://data.WorldBank.org/

  34. http://www.oanda.com/currency/historical-rates/

  35. M. J. Naylora, L. C. Rose, and B. J. Moyle, Topology of foreign exchange markets using hierarchical structure methods, Physica A, 2007, 382(1): 199

    Article  MathSciNet  ADS  Google Scholar 

  36. R. Rammel, G. Toulouse, and M. A. Virasoro, Ultrametricity for physicists, Rev. Mod. Phys., 1986, 58(3): 765

    Article  ADS  Google Scholar 

  37. J. P. Onnela and M. Sc, Taxonomy of Financial Assets, Thesis, Helsinki University of Technology, 2002

    Google Scholar 

  38. D. M. Song, M. Tumminello, W. X. Zhou, and R. N. Mantegna, Evolution of worldwide stock markets, correlation structure, and correlation-based graphs, Phys. Rev. E, 2011, 84(2): 026108

    Article  ADS  Google Scholar 

  39. M. Eryiǧit and R. Eryiǧit, Network structure of crosscorrelations among the world market indices, Physica A, 2009, 388(17): 3551

    Article  ADS  Google Scholar 

  40. G. Bonanno, N. Vandewalle, and R. N. Mantegna, Taxonomy of stock market indices, Phys. Rev. E, 2000, 62(6): R7615

    Article  ADS  Google Scholar 

  41. R. Coelho, C. G. Gilmore, B. Lucey, P. Richmond, and S. Hutzler, The evolution of interdependence in world equity markets — Evidence from minimum spanning trees, Physica A, 2007, 376: 455

    Article  ADS  Google Scholar 

  42. E. Kantar, B. Deviren, and M. Keskin, Hierarchical structure of Turkey’s foreign trade, Physica A, 2011, 390(20): 3454

    Article  ADS  Google Scholar 

  43. Y. Kocakaplan, Ş. Doǧan, B. Deviren, and M. Keskin, Correlations, hierarchies and networks of the world’s automotive companies, Physica A, 2013, 392(12): 2736

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physics and the Beijing-Hong Kong-Singapore Joint Center for Nonlinear and Complex Systems, Beijing Normal University, Beijing, 100875, China

    Xin-Ye Wu & Zhi-Gang Zheng

Authors
  1. Xin-Ye Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi-Gang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi-Gang Zheng.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Wu, XY., Zheng, ZG. Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market. Front. Phys. 8, 451–460 (2013). https://doi.org/10.1007/s11467-013-0346-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11467-013-0346-4

Keywords

  • foreign-exchange market
  • hierarchical cluster-tendency method
  • hierarchical tree
  • minimum spanning tree