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Quantifying volatility clustering in financial time series

Listed author(s):
  • Tseng, Jie-Jun
  • Li, Sai-Ping
Registered author(s):

    A quantitative method is introduced in this work to quantify and compare the volatility clustering behavior among various financial time series. In addition to financial markets, our approach can also be applied to other complex systems and we take the earthquake as an example to demonstrate the applicability of our approach. We further propose a toy model which can mimic the stylized facts in financial markets. This model could be interpreted as the accumulation effect of the news impact on the price fluctuation in a financial market and can be viewed as a first step towards understanding the complex market behavior.

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    File URL: http://www.sciencedirect.com/science/article/pii/S1057521911000780
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    Article provided by Elsevier in its journal International Review of Financial Analysis.

    Volume (Year): 23 (2012)
    Issue (Month): C ()
    Pages: 11-19

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    Handle: RePEc:eee:finana:v:23:y:2012:i:c:p:11-19
    DOI: 10.1016/j.irfa.2011.06.017
    Contact details of provider: Web page: http://www.elsevier.com/locate/inca/620166

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    1. Yanhui Liu & Parameswaran Gopikrishnan & Pierre Cizeau & Martin Meyer & Chung-Kang Peng & H. Eugene Stanley, 1999. "The statistical properties of the volatility of price fluctuations," Papers cond-mat/9903369, arXiv.org, revised Mar 1999.
    2. Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, vol. 36, pages 394-394.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    4. Joseph Chen & Harrison Hong & Jeremy C. Stein, 2000. "Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices," NBER Working Papers 7687, National Bureau of Economic Research, Inc.
    5. Matteo Pelagatti, 2007. "Modelling good and bad volatility," Working Papers 20071101, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
    6. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    7. Tseng, Jie-Jun & Li, Sai-Ping, 2011. "Asset returns and volatility clustering in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1300-1314.
    8. M. Cristelli & L. Pietronero & A. Zaccaria, 2011. "Critical Overview of Agent-Based Models for Economics," Papers 1101.1847, arXiv.org.
    9. R. F. Engle & A. J. Patton, 2001. "What good is a volatility model?," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 237-245.
    10. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    11. Jie-Jun Tseng & Sai-Ping Li, 2010. "Asset returns and volatility clustering in financial time series," Papers 1002.0284, arXiv.org, revised Apr 2011.
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