IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v45y2012i6p838-845.html
   My bibliography  Save this article

Time-clustering behavior of sharp fluctuation sequences in Chinese stock markets

Author

Listed:
  • Yuan, Ying
  • Zhuang, Xin-tian
  • Liu, Zhi-ying
  • Huang, Wei-qiang

Abstract

Sharp fluctuations (in particular, extreme fluctuations) of asset prices have a great impact on financial markets and risk management. Therefore, investigating the time dynamics of sharp fluctuation is a challenge in the financial fields. Using two different representations of the sharp fluctuations (inter-event times and series of counts), the time clustering behavior in the sharp fluctuation sequences of stock markets in China is studied with several statistical tools, including coefficient of variation, Allan Factor, Fano Factor as well as R/S (rescaled range) analysis. All of the empirical results indicate that the time dynamics of the sharp fluctuation sequences can be considered as a fractal process with a high degree of time-clusterization of the events. It can help us to get a better understanding of the nature and dynamics of sharp fluctuation of stock price in stock markets.

Suggested Citation

  • Yuan, Ying & Zhuang, Xin-tian & Liu, Zhi-ying & Huang, Wei-qiang, 2012. "Time-clustering behavior of sharp fluctuation sequences in Chinese stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 45(6), pages 838-845.
    • Handle: RePEc:eee:chsofr:v:45:y:2012:i:6:p:838-845
    DOI: 10.1016/j.chaos.2012.02.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077912000719
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2012.02.020?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. Gencay, Ramazan & Selcuk, Faruk, 2004. "Extreme value theory and Value-at-Risk: Relative performance in emerging markets," International Journal of Forecasting, Elsevier, vol. 20(2), pages 287-303.
    2. Telesca, Luciano & Lasaponara, Rosa, 2010. "Analysis of time-scaling properties in forest-fire sequence observed in Italy," Ecological Modelling, Elsevier, vol. 221(1), pages 90-93.
    3. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    4. Bali, Turan G. & Neftci, Salih N., 2003. "Disturbing extremal behavior of spot rate dynamics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 455-477, September.
    5. Yuan, Ying & Zhuang, Xin-tian & Jin, Xiu, 2009. "Measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(11), pages 2189-2197.
    6. Lasaponara, Rosa & Santulli, Adriano & Telesca, Luciano, 2005. "Time-clustering analysis of forest-fire sequences in southern Italy," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 139-149.
    7. Telesca, Luciano & Lovallo, Michele, 2007. "Non-random components in aircraft accidents time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 407-410.
    8. Tim Krehbiel & Lee C. Adkins, 2005. "Price risk in the NYMEX energy complex: An extreme value approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(4), pages 309-337, April.
    9. Telesca, Luciano, 2007. "Identifying time-clustering structures in the sequence of solar flare hard X-ray bursts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 516-521.
    10. Telesca, Luciano & Song, Weiguo, 2011. "Time-scaling properties of city fires," Chaos, Solitons & Fractals, Elsevier, vol. 44(7), pages 558-568.
    11. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    12. Telesca, Luciano & Lovallo, Michele, 2008. "Analysis of the temporal properties in car accident time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3299-3304.
    13. Longin, Francois M., 2000. "From value at risk to stress testing: The extreme value approach," Journal of Banking & Finance, Elsevier, vol. 24(7), pages 1097-1130, July.
    14. Telesca, Luciano & Lovallo, Michele, 2006. "Are global terrorist attacks time-correlated?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 480-484.
    15. Chen, Huiping & Sun, Xia & Wu, Ziqin & Wang, Binghong, 2004. "Enlightenment from various conditional probabilities about Hang Seng index in Hong Kong stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 183-196.
    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. Serinaldi, Francesco & Kilsby, Chris G., 2013. "On the sampling distribution of Allan factor estimator for a homogeneous Poisson process and its use to test inhomogeneities at multiple scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1080-1089.
    2. Contreras-Uribe, T.J. & Garay-Jiménez, L.I. & Guzmán-Vargas, L., 2017. "A point process analysis of electrogastric variability," Chaos, Solitons & Fractals, Elsevier, vol. 94(C), pages 16-22.
    3. Cao, Guangxi & Zhang, Minjia, 2015. "Extreme values in the Chinese and American stock markets based on detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 25-35.
    4. Yuan, Ying & Zhuang, Xin-tian & Liu, Zhi-ying & Huang, Wei-qiang, 2014. "Analysis of the temporal properties of price shock sequences in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 235-246.

    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. Yuan, Ying & Zhuang, Xin-tian & Liu, Zhi-ying & Huang, Wei-qiang, 2014. "Analysis of the temporal properties of price shock sequences in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 235-246.
    2. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    3. Telesca, Luciano & Song, Weiguo, 2011. "Time-scaling properties of city fires," Chaos, Solitons & Fractals, Elsevier, vol. 44(7), pages 558-568.
    4. Eftaxias, Konstantinos & Minadakis, George & Potirakis, Stelios. M. & Balasis, Georgios, 2013. "Dynamical analogy between epileptic seizures and seismogenic electromagnetic emissions by means of nonextensive statistical mechanics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 497-509.
    5. de Benicio, Rosilda B. & Stošić, Tatijana & de Figueirêdo, P.H. & Stošić, Borko D., 2013. "Multifractal behavior of wild-land and forest fire time series in Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6367-6374.
    6. Youssef, Manel & Belkacem, Lotfi & Mokni, Khaled, 2015. "Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach," Energy Economics, Elsevier, vol. 51(C), pages 99-110.
    7. Vêlayoudom Marimoutou & Bechir Raggad & Abdelwahed Trabelsi, 2006. "Extreme Value Theory and Value at Risk : Application to Oil Market," Working Papers halshs-00410746, HAL.
    8. Krehbiel, Tim & Adkins, Lee C., 2008. "Extreme daily changes in U.S. Dollar London inter-bank offer rates," International Review of Economics & Finance, Elsevier, vol. 17(3), pages 397-411.
    9. Alexander, Carol & Sheedy, Elizabeth, 2008. "Developing a stress testing framework based on market risk models," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2220-2236, October.
    10. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
    11. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    12. Tolikas, Konstantinos, 2014. "Unexpected tails in risk measurement: Some international evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 476-493.
    13. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.
    14. Karmakar, Madhusudan & Shukla, Girja K., 2015. "Managing extreme risk in some major stock markets: An extreme value approach," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 1-25.
    15. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    16. Lucjan T. Orlowski, 2012. "Financial crisis and extreme market risks: Evidence from Europe," Review of Financial Economics, John Wiley & Sons, vol. 21(3), pages 120-130, September.
    17. Allen, Linda & Bali, Turan G., 2007. "Cyclicality in catastrophic and operational risk measurements," Journal of Banking & Finance, Elsevier, vol. 31(4), pages 1191-1235, April.
    18. Riedel, Christoph & Wagner, Niklas, 2015. "Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 53-64.
    19. 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.
    20. Paraschiv, Florentina & Mudry, Pierre-Antoine & Andries, Alin Marius, 2015. "Stress-testing for portfolios of commodity futures," Economic Modelling, Elsevier, vol. 50(C), pages 9-18.

    More about this item

    Statistics

    Access and download statistics

    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:chsofr:v:45:y:2012:i:6:p:838-845. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.