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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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    References listed on IDEAS

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    1. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169, January.
    2. 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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    Citations

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    Cited by:

    1. Andrea Giuseppe Di Iura & Giulia Terenzi, 2021. "A Bayesian analysis of gain-loss asymmetry," Papers 2104.06044, arXiv.org.
    2. Venelina Nikolova & Juan E. Trinidad Segovia & Manuel Fernández-Martínez & Miguel Angel Sánchez-Granero, 2020. "A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets," Mathematics, MDPI, vol. 8(8), pages 1-15, July.
    3. 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.
    4. 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.
    5. María Nieves López-García & Miguel Angel Sánchez-Granero & Juan Evangelista Trinidad-Segovia & Antonio Manuel Puertas & Francisco Javier De las Nieves, 2021. "Volatility Co-Movement in Stock Markets," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    6. Zhao, Xiaojun & Zhang, Na & Zhang, Yali & Xu, Chao & Shang, Pengjian, 2024. "Equity markets volatility clustering: A multiscale analysis of intraday and overnight returns," Journal of Empirical Finance, Elsevier, vol. 77(C).
    7. 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.
    8. C. M. Rodr'iguez-Mart'inez & H. F. Coronel-Brizio & A. R. Hern'andez-Montoya, 2019. "A multi-scale symmetry analysis of uninterrupted trends returns of daily financial indices," Papers 1908.11204, arXiv.org.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Michele Berardi, 2016. "Endogenous time-varying risk aversion and asset returns," Journal of Evolutionary Economics, Springer, vol. 26(3), pages 581-601, July.
    14. 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.
    15. Trinidad Segovia, J.E. & Fernández-Martínez, M. & Sánchez-Granero, M.A., 2019. "A novel approach to detect volatility clusters in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    16. Xing, Yani & Wang, Jun, 2019. "Statistical volatility duration and complexity of financial dynamics on Sierpinski gasket lattice percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 234-247.
    17. An, Sufang & Gao, Xiangyun & Jiang, Meihui & Sun, Xiaoqi, 2018. "Multivariate financial time series in the light of complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1241-1255.
    18. Andrea Di Iura & Giulia Terenzi, 2022. "A Bayesian analysis of gain-loss asymmetry," SN Business & Economics, Springer, vol. 2(5), pages 1-23, May.
    19. Rodríguez-Martínez, C.M. & Coronel-Brizio, H.F. & Hernández-Montoya, A.R., 2021. "A multi-scale symmetry analysis of uninterrupted trends returns in daily financial indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    20. 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.

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