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Interest rates hierarchical structure

Author

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  • Di Matteo, T.
  • Aste, T.
  • Hyde, S.T.
  • Ramsden, S.

Abstract

We propose a general method to study the hierarchical organization of financial data by embedding the structure of their correlations in metric graphs in multi-dimensional spaces. An application to two different sets of interest rates is discussed by constructing triangular embeddings on the sphere. Three-dimensional representations of these embeddings with the correct metric geometry are constructed and visualized. The resulting graphs contain the minimum spanning tree as a sub-graph and they preserve its hierarchical structure. This produces a clear cluster differentiation and allows us to compute new local and global topological quantities.

Suggested Citation

  • Di Matteo, T. & Aste, T. & Hyde, S.T. & Ramsden, S., 2005. "Interest rates hierarchical structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 21-33.
    • Handle: RePEc:eee:phsmap:v:355:y:2005:i:1:p:21-33
    DOI: 10.1016/j.physa.2005.02.063
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    References listed on IDEAS

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    1. Pagan, A.R. & Hall, A.D. & Martin, V., 1995. "Modelling the Term Structure," Papers 284, Australian National University - Department of Economics.
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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. Nicolo Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," Papers 1406.0496, arXiv.org, revised Jan 2015.
    3. Kanevski, M. & Maignan, M. & Pozdnoukhov, A. & Timonin, V., 2008. "Interest rates mapping," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3897-3903.
    4. Xie Chi & Zhou Yingying & Wang Gangjin & Yan Xinguo, 2017. "Investigating the Disparities of China’s Insurance Market Based on Minimum Spanning Tree from the Viewpoint of Geography and Enterprise," Journal of Systems Science and Information, De Gruyter, vol. 5(3), pages 216-228, June.
    5. Pang, Raymond Ka-Kay & Granados, Oscar M. & Chhajer, Harsh & Legara, Erika Fille T., 2021. "An analysis of network filtering methods to sovereign bond yields during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    6. Tabak, Benjamin M. & Luduvice, André Victor D. & Cajueiro, Daniel O., 2011. "Modeling default probabilities: The case of Brazil," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(4), pages 513-534, October.
    7. Musmeci, Nicoló & Aste, Tomaso & Di Matteo, T., 2015. "Relation between financial market structure and the real economy: comparison between clustering methods," LSE Research Online Documents on Economics 61644, London School of Economics and Political Science, LSE Library.
    8. Aste, T. & Di Matteo, T., 2006. "Dynamical networks from correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 156-161.
    9. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    10. Lee, Junghoon & Youn, Janghyuk & Chang, Woojin, 2012. "Intraday volatility and network topological properties in the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1354-1360.
    11. Raymond Ka-Kay Pang & Oscar Granados & Harsh Chhajer & Erika Fille Legara, 2020. "An analysis of network filtering methods to sovereign bond yields during COVID-19," Papers 2009.13390, arXiv.org, revised Feb 2021.
    12. Nicol'o Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Risk diversification: a study of persistence with a filtered correlation-network approach," Papers 1410.5621, arXiv.org.
    13. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.

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