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Levels of complexity in financial markets

Author

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  • Bonanno, Giovanni
  • Lillo, Fabrizio
  • Mantegna, Rosario N.

Abstract

We consider different levels of complexity which are observed in the empirical investigation of financial time series. We discuss recent empirical and theoretical work showing that statistical properties of financial time series are rather complex under several ways. Specifically, they are complex with respect to their (i) temporal and (ii) ensemble properties. Moreover, the ensemble return properties show a behavior which is specific to the nature of the trading day reflecting if it is a normal or an extreme trading day.

Suggested Citation

  • Bonanno, Giovanni & Lillo, Fabrizio & Mantegna, Rosario N., 2001. "Levels of complexity in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 16-27.
  • Handle: RePEc:eee:phsmap:v:299:y:2001:i:1:p:16-27
    DOI: 10.1016/S0378-4371(01)00279-5
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    Cited by:

    1. Andreia Dionisio & Rui Menezes & Diana A. Mendes, 2003. "Mutual information: a dependence measure for nonlinear time series," Econometrics 0311003, EconWPA.
    2. 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 Sep 2017.
    3. David Matesanz & Guillermo Ortega, 2014. "Network analysis of exchange data: interdependence drives crisis contagion," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 1835-1851, July.
    4. Miśkiewicz, Janusz, 2013. "Power law classification scheme of time series correlations. On the example of G20 group," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2150-2162.
    5. repec:eee:touman:v:31:y:2010:i:1:p:57-73 is not listed on IDEAS
    6. Gábor Dávid Kiss & Tamás Schuszter, 2015. "The Euro Crisis and Contagion among Central and Eastern European Currencies: Recommendations for Avoiding Lending in a Safe Haven Currency such as CHF," Prague Economic Papers, University of Economics, Prague, vol. 2015(6), pages 678-698.
    7. Matesanz, David & Ortega, Guillermo J., 2015. "Sovereign public debt crisis in Europe. A network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 756-766.
    8. Anna CZAPKIEWICZ & Pawel MAJDOSZ, 2014. "Grouping Stock Markets with Time-Varying Copula-GARCH Model," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(2), pages 144-159, March.
    9. Bertrand M. Roehner, 2004. "Stock markets are not what we think they are: the key roles of cross-ownership and corporate treasury stock," Papers cond-mat/0406704, arXiv.org.
    10. Roehner, Bertrand M., 2005. "Stock markets are not what we think they are: the key roles of cross-ownership and corporate treasury stock," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 347(C), pages 613-625.
    11. Tanya Ara'ujo & Francisco Louc{c}~a, 2004. "Complex Behavior of Stock Markets: Processes of Synchronization and Desynchronization during Crises," Papers cond-mat/0403333, arXiv.org, revised Mar 2004.
    12. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    13. Tanya Araujo & Francisco Louca, 2007. "The geometry of crashes. A measure of the dynamics of stock market crises," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 63-74.
    14. Gábor Dávid Kiss & Andreász Kosztopulosz, 2012. "The impact of the crisis on the monetary autonomy of Central and Eastern European countries," Public Finance Quarterly, State Audit Office of Hungary, vol. 57(1), pages 28-52.
    15. 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.
    16. Wei, Yu & Chen, Wang & Lin, Yu, 2013. "Measuring daily Value-at-Risk of SSEC index: A new approach based on multifractal analysis and extreme value theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2163-2174.
    17. Civitarese, Jamil, 2016. "Volatility and correlation-based systemic risk measures in the US market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 55-67.
    18. Chen, Wang & Wei, Yu & Lang, Qiaoqi & Lin, Yu & Liu, Maojuan, 2014. "Financial market volatility and contagion effect: A copula–multifractal volatility approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 289-300.
    19. Brida, Juan Gabriel & Risso, Wiston Adrián, 2008. "Multidimensional minimal spanning tree: The Dow Jones case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5205-5210.
    20. Hokky Situngkir & Yohanes Surya, 2005. "On Stock Market Dynamics through Ultrametricity of Minimum Spanning Tree," Macroeconomics 0505010, EconWPA.
    21. Goswami, B. & Ambika, G. & Marwan, N. & Kurths, J., 2012. "On interrelations of recurrences and connectivity trends between stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4364-4376.
    22. repec:prg:jnlpep:v:preprint:id:530:p:1-15 is not listed on IDEAS
    23. repec:eee:apmaco:v:285:y:2016:i:c:p:103-113 is not listed on IDEAS
    24. Tanya Ara'ujo & Francisco Louc{c}~a, 2005. "The Geometry of Crashes - A Measure of the Dynamics of Stock Market Crises," Papers physics/0506137, arXiv.org, revised Jul 2005.

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