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Asset returns and volatility clustering in financial time series

Author

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  • Jie-Jun Tseng
  • Sai-Ping Li

Abstract

An analysis of the stylized facts in financial time series is carried out. We find that, instead of the heavy tails in asset return distributions, the slow decay behaviour in autocorrelation functions of absolute returns is actually directly related to the degree of clustering of large fluctuations within the financial time series. We also introduce an index to quantitatively measure the clustering behaviour of fluctuations in these time series and show that big losses in financial markets usually lump more severely than big gains. We further give examples to demonstrate that comparing to conventional methods, our index enables one to extract more information from the financial time series.

Suggested Citation

  • Jie-Jun Tseng & Sai-Ping Li, 2010. "Asset returns and volatility clustering in financial time series," Papers 1002.0284, arXiv.org, revised Apr 2011.
  • Handle: RePEc:arx:papers:1002.0284
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    File URL: http://arxiv.org/pdf/1002.0284
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    References listed on IDEAS

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    1. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2009. "Econophysics: Empirical facts and agent-based models," Papers 0909.1974, arXiv.org, revised Jun 2010.
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    Cited by:

    1. Tseng, Jie-Jun & Li, Sai-Ping, 2012. "Quantifying volatility clustering in financial time series," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 11-19.
    2. Michele Berardi, 2016. "Endogenous time-varying risk aversion and asset returns," Journal of Evolutionary Economics, Springer, vol. 26(3), pages 581-601, July.
    3. Ross, Gordon J., 2013. "Modelling financial volatility in the presence of abrupt changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(2), pages 350-360.
    4. Chunxia, Yang & Bingying, Xia & Sen, Hu & Rui, Wang, 2012. "A study of the interplay between the structure variation and fluctuations of the Shanghai stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3198-3205.
    5. Kim, Kyungwon, 2013. "Modeling financial crisis period: A volatility perspective of Credit Default Swap market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 4977-4988.
    6. D’Urso, Pierpaolo & Cappelli, Carmela & Di Lallo, Dario & Massari, Riccardo, 2013. "Clustering of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2114-2129.
    7. Jung, Sean S. & Chang, Woojin, 2016. "Clustering stocks using partial correlation coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 410-420.
    8. Cao, Guangxi & Zhang, Minjia & Li, Qingchen, 2017. "Volatility-constrained multifractal detrended cross-correlation analysis: Cross-correlation among Mainland China, US, and Hong Kong stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 67-76.
    9. Yang, ChunXia & Hu, Sen & Xia, BingYing, 2012. "The endogenous dynamics of financial markets: Interaction and information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(12), pages 3513-3525.
    10. Suo, Yuan-Yuan & Wang, Dong-Hua & Li, Sai-Ping, 2015. "Risk estimation of CSI 300 index spot and futures in China from a new perspective," Economic Modelling, Elsevier, vol. 49(C), pages 344-353.

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