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A novel approach to detect volatility clusters in financial time series

Author

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  • Trinidad Segovia, J.E.
  • Fernández-Martínez, M.
  • Sánchez-Granero, M.A.

Abstract

The self-similarity index has been consolidated as a widely applied measure to quantify long-memory in stock markets. In this article, though, we shall provide a novel methodology allowing the detection of clusters of volatility in series of asset returns. With this aim, the concept of a volatility series is introduced. We found that the existence of clusters of high/low volatility in the series leads to an increasing Hurst exponent of the volatility series.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119314098
    DOI: 10.1016/j.physa.2019.122452
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