IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v324y2003i1p66-73.html
   My bibliography  Save this article

Degree stability of a minimum spanning tree of price return and volatility

Author

Listed:
  • Miccichè, Salvatore
  • Bonanno, Giovanni
  • Lillo, Fabrizio
  • N. Mantegna, Rosario

Abstract

We investigate the time series of the degree of minimum spanning trees (MSTs) obtained by using a correlation-based clustering procedure which starts from (i) asset return and (ii) volatility time series. The MST is obtained at different times by computing correlation among time series over a time window of fixed length T. We find that the MST of asset return is characterized by stock degree values, which are more stable in time than the ones obtained by analyzing a MST computed starting from volatility time series. Our analysis also shows that the degree of stocks has a very slow dynamics with a time scale of several years in both cases.

Suggested Citation

  • Miccichè, Salvatore & Bonanno, Giovanni & Lillo, Fabrizio & N. Mantegna, Rosario, 2003. "Degree stability of a minimum spanning tree of price return and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 66-73.
    • Handle: RePEc:eee:phsmap:v:324:y:2003:i:1:p:66-73
    DOI: 10.1016/S0378-4371(03)00002-5
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437103000025
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/S0378-4371(03)00002-5?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Onnela, J.-P. & Chakraborti, A. & Kaski, K. & Kertész, J., 2003. "Dynamic asset trees and Black Monday," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 247-252.
    Full references (including those not matched with items on IDEAS)

    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. D'Arcangelis, Anna Maria & Rotundo, Giulia, 2021. "Herding in mutual funds: A complex network approach," Journal of Business Research, Elsevier, vol. 129(C), pages 679-686.
    2. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    3. Sandoval, Leonidas, 2014. "To lag or not to lag? How to compare indices of stock markets that operate on different times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 227-243.
    4. Linda Margarita Medina Herrera & José Benito Díaz Hernández, 2011. "Caracterización y modelado de redes: el caso de la Bolsa Mexicana de Valores," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 5(1), pages 23-32.
    5. Výrost, Tomáš, 2012. "Country effects in CEE3 stock market networks: a preliminary study," MPRA Paper 43481, University Library of Munich, Germany.
    6. Teh, Boon Kin & Goo, Yik Wen & Lian, Tong Wei & Ong, Wei Guang & Choi, Wen Ting & Damodaran, Mridula & Cheong, Siew Ann, 2015. "The Chinese Correction of February 2007: How financial hierarchies change in a market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 225-241.
    7. Chen, Wei & Hou, Xiaoli & Jiang, Manrui & Jiang, Cheng, 2022. "Identifying systemically important financial institutions in complex network: A case study of Chinese stock market," Emerging Markets Review, Elsevier, vol. 50(C).
    8. 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.
    9. Leonidas Sandoval Junior, 2011. "Cluster formation and evolution in networks of financial market indices," Papers 1111.5069, arXiv.org.
    10. Yangguang Zhu & Feng Yang & Wuyi Ye, 2018. "Financial contagion behavior analysis based on complex network approach," Annals of Operations Research, Springer, vol. 268(1), pages 93-111, September.
    11. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.
    12. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    13. Precup, O. V. & Iori, G., 2005. "Cross-correlation measures in the high-frequency domain," Working Papers 05/04, Department of Economics, City University London.
    14. Frank Emmert-Streib & Aliyu Musa & Kestutis Baltakys & Juho Kanniainen & Shailesh Tripathi & Olli Yli-Harja & Herbert Jodlbauer & Matthias Dehmer, 2017. "Computational Analysis of the structural properties of Economic and Financial Networks," Papers 1710.04455, arXiv.org.
    15. Sayantan Banerjee & Kousik Guhathakurta, 2019. "Change-point Analysis in Financial Networks," Papers 1911.05952, arXiv.org.
    16. A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Dynamic structural and topological phase transitions on the Warsaw Stock Exchange: A phenomenological approach," Papers 1301.6506, arXiv.org.
    17. 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.
    18. Heiberger, Raphael H., 2014. "Stock network stability in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 376-381.
    19. 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.
    20. Fatih Cavdur & Soundar Kumara, 2014. "Network mining: Applications to business data," Information Systems Frontiers, Springer, vol. 16(3), pages 473-490, July.

    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:eee:phsmap:v:324:y:2003:i:1:p:66-73. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.