A novel approach to detect volatility clusters in financial time series

J.E. Trinidad Segovia, M. Fernández-Martínez and M.A. Sánchez-Granero
Authors registered in the RePEc Author Service: Juan Evangelista Trinidad-Segovia ()

Physica A: Statistical Mechanics and its Applications, 2019, vol. 535, issue C

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.

Keywords: Hurst exponent; Volatility cluster; Volatility series; FD4 algorithm; S&P500 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119314098

DOI: 10.1016/j.physa.2019.122452

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