IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v456y2016icp310-318.html
   My bibliography  Save this article

Clustering of Casablanca stock market based on hurst exponent estimates

Author

Listed:
  • Lahmiri, Salim

Abstract

This paper deals with the problem of Casablanca Stock Exchange (CSE) topology modeling as a complex network during three different market regimes: general trend characterized by ups and downs, increasing trend, and decreasing trend. In particular, a set of seven different Hurst exponent estimates are used to characterize long-range dependence in each industrial sector generating process. They are employed in conjunction with hierarchical clustering approach to examine the co-movements of the Casablanca Stock Exchange industrial sectors. The purpose is to investigate whether cluster structures are similar across variable, increasing and decreasing regimes. It is observed that the general structure of the CSE topology has been considerably changed over 2009 (variable regime), 2010 (increasing regime), and 2011 (decreasing regime) time periods. The most important findings follow. First, in general a high value of Hurst exponent is associated to a variable regime and a small one to a decreasing regime. In addition, Hurst estimates during increasing regime are higher than those of a decreasing regime. Second, correlations between estimated Hurst exponent vectors of industrial sectors increase when Casablanca stock exchange follows an upward regime, whilst they decrease when the overall market follows a downward regime.

Suggested Citation

  • Lahmiri, Salim, 2016. "Clustering of Casablanca stock market based on hurst exponent estimates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 310-318.
    • Handle: RePEc:eee:phsmap:v:456:y:2016:i:c:p:310-318
    DOI: 10.1016/j.physa.2016.03.069
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711630067X
    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/j.physa.2016.03.069?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 search for a different version of it.

    References listed on IDEAS

    as
    1. G. Bonanno & F. Lillo & R. N. Mantegna, 2001. "High-frequency cross-correlation in a set of stocks," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 96-104.
    2. Matcharashvili, Teimuraz & Chelidze, Tamaz & Zhukova, Natalia, 2015. "Assessment of the relative ratio of correlated and uncorrelated waiting times in the Southern California earthquakes catalogue," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 291-303.
    3. Jiang, J. & Ma, K. & Cai, X., 2007. "Non-linear characteristics and long-range correlations in Asian stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 399-407.
    4. Wang, Shifang & Wu, Tao & Qi, Hongyan & Zheng, Qiusha & Zheng, Qian, 2015. "A permeability model for power-law fluids in fractal porous media composed of arbitrary cross-section capillaries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 12-20.
    5. 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.
    6. 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.
    7. Ladislav Kristoufek & Miloslav Vosvrda, 2014. "Measuring capital market efficiency: long-term memory, fractal dimension and approximate entropy," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(7), pages 1-9, July.
    8. Giovanni Bonanno & Nicolas Vandewalle & Rosario N. Mantegna, 2000. "Taxonomy of Stock Market Indices," Papers cond-mat/0001268, arXiv.org, revised Aug 2000.
    9. Lahmiri, Salim, 2015. "Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 130-138.
    10. B. M. Tabak & T. R. Serra & D. O. Cajueiro, 2010. "Topological properties of commodities networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 74(2), pages 243-249, March.
    11. Barunik, Jozef & Kristoufek, Ladislav, 2010. "On Hurst exponent estimation under heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3844-3855.
    12. Zhou, Yuan-Wu & Liu, Jin-Long & Yu, Zu-Guo & Zhao, Zhi-Qin & Anh, Vo, 2014. "Fractal and complex network analyses of protein molecular dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 21-32.
    13. Liu, Shuang & Jia, Guo-zhu & Zhang, Shu, 2016. "Consideration of fractal and ion–water cooperative interactions in aqueous Na2SO4 and K2SO4 solutions by dielectric relaxation spectroscopy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 15-22.
    14. Conde-Saavedra, G. & Iribarrem, A. & Ribeiro, Marcelo B., 2015. "Fractal analysis of the galaxy distribution in the redshift range 0.45≤z≤5.0," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 332-344.
    15. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2009. "The expectation hypothesis of interest rates and network theory: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1137-1149.
    16. Ghosh, Sayantan & Manimaran, P. & Panigrahi, Prasanta K., 2011. "Characterizing multi-scale self-similar behavior and non-statistical properties of fluctuations in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4304-4316.
    17. Caraiani, Petre & Haven, Emmanuel, 2015. "Evidence of multifractality from CEE exchange rates against Euro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 395-407.
    18. Kristoufek, Ladislav & Vosvrda, Miloslav, 2013. "Measuring capital market efficiency: Global and local correlations structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 184-193.
    19. Salim Lahmiri, 2014. "Multi-Scaling Analysis of the S&P500 under Different Regimes in Wavelet Domain," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 5(2), pages 43-55, April.
    20. Jang, Wooseok & Lee, Junghoon & Chang, Woojin, 2011. "Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 707-718.
    21. Phillips, J.C., 2014. "Fractals and self-organized criticality in proteins," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 440-448.
    22. Cao, Guangxi & Cao, Jie & Xu, Longbing, 2013. "Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 797-807.
    23. Souza, J.W.G. & Santos, A.A.B. & Guarieiro, L.L.N. & Moret, M.A., 2015. "Fractal aspects in O2 enriched combustion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 268-272.
    24. Vadim Teverovsky & Murad Taqqu, 1997. "Testing for long‐range dependence in the presence of shifting means or a slowly declining trend, using a variance‐type estimator," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(3), pages 279-304, May.
    25. 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.
    26. Ghosal, Subhasish & Mukhopadhyay, Madhumita & Ray, Ruma & Tarafdar, Sujata, 2014. "Competitive scission and cross linking in a solid polymer electrolyte exposed to gamma irradiation: Simulation by a fractal model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 139-150.
    27. Mizuno, Takayuki & Takayasu, Hideki & Takayasu, Misako, 2006. "Correlation networks among currencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 336-342.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lahmiri, Salim & Bekiros, Stelios & Bezzina, Frank, 2020. "Multi-fluctuation nonlinear patterns of European financial markets based on adaptive filtering with application to family business, green, Islamic, common stocks, and comparison with Bitcoin, NASDAQ, ," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    2. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    3. Lahmiri, Salim, 2017. "On fractality and chaos in Moroccan family business stock returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 29-39.
    4. Zhang, Bo & Wang, Guochao & Wang, Yiduan & Zhang, Wei & Wang, Jun, 2019. "Multiscale statistical behaviors for Ising financial dynamics with continuum percolation jump," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1012-1025.
    5. Lahmiri, Salim, 2017. "Multifractal analysis of Moroccan family business stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 183-191.
    6. Grobys, Klaus, 2023. "A Fractal and Comparative View of the Memory of Bitcoin and S&P 500 Returns," Research in International Business and Finance, Elsevier, vol. 66(C).
    7. Luis A. Gil‐Alana & Robert Mudida & OlaOluwa S. Yaya & Kazeem A. Osuolale & Ahamuefula E. Ogbonna, 2021. "Mapping US presidential terms with S&P500 index: Time series analysis approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1938-1954, April.
    8. Lahmiri, Salim & Bekiros, Stelios, 2020. "Nonlinear analysis of Casablanca Stock Exchange, Dow Jones and S&P500 industrial sectors with a comparison," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    9. Zhang, Hong-Yan & Kang, Ming-Cui & Li, Jing-Qiang & Liu, Hai-Tao, 2017. "R/S analysis of reaction time in Neuron Type Test for human activity in civil aviation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 859-870.
    10. Blanka Horvath & Zacharia Issa & Aitor Muguruza, 2021. "Clustering Market Regimes using the Wasserstein Distance," Papers 2110.11848, arXiv.org.
    11. Lahmiri, Salim & Uddin, Gazi Salah & Bekiros, Stelios, 2017. "Clustering of short and long-term co-movements in international financial and commodity markets in wavelet domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 947-955.
    12. Jang, Hanwool & Song, Yena & Ahn, Kwangwon, 2020. "Can government stabilize the housing market? The evidence from South Korea," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).

    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. 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. 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.
    3. 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.
    4. Lahmiri, Salim, 2017. "On fractality and chaos in Moroccan family business stock returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 29-39.
    5. 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.
    6. 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.
    7. Lahmiri, Salim, 2015. "Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 130-138.
    8. Kazemilari, Mansooreh & Mardani, Abbas & Streimikiene, Dalia & Zavadskas, Edmundas Kazimieras, 2017. "An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach," Renewable Energy, Elsevier, vol. 102(PA), pages 107-117.
    9. 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.
    10. 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.
    11. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    12. 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.
    13. Peng Yue & Qing Cai & Wanfeng Yan & Wei-Xing Zhou, 2020. "Information flow networks of Chinese stock market sectors," Papers 2004.08759, arXiv.org.
    14. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    15. Zhang, Xin & Podobnik, Boris & Kenett, Dror Y. & Eugene Stanley, H., 2014. "Systemic risk and causality dynamics of the world international shipping market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 43-53.
    16. Kristoufek, Ladislav & Vosvrda, Miloslav, 2016. "Gold, currencies and market efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 27-34.
    17. Sandoval, Leonidas Junior, 2013. "To lag or not to lag? How to compare indices of stock markets that operate at different times," Insper Working Papers wpe_319, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    18. Lahmiri, Salim & Uddin, Gazi Salah & Bekiros, Stelios, 2017. "Clustering of short and long-term co-movements in international financial and commodity markets in wavelet domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 947-955.
    19. Li, Daye & Nishimura, Yusaku & Men, Ming, 2016. "The long memory and the transaction cost in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 312-320.
    20. Li, Daye & Kou, Zhun & Sun, Qiankun, 2015. "The scale-dependent market trend: Empirical evidences using the lagged DFA method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 26-35.

    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:456:y:2016:i:c:p:310-318. 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.