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Detecting market crashes by analysing long-memory effects using high-frequency data

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  • E. Barany
  • M. P. Beccar Varela
  • I. Florescu
  • I. Sengupta

Abstract

It is well known that returns for financial data sampled with high frequency exhibit memory effects, in contrast to the behavior of the much celebrated log-normal model. Herein, we analyse minute data for several stocks over a seven-day period which we know is relevant for market crash behavior in the US market, March 10--18, 2008. We look at the relationship between the Lévy parameter α characterizing the data and the resulting H parameter characterizing the self-similar property. We give an estimate of how close this model is to a self-similar model.

Suggested Citation

  • E. Barany & M. P. Beccar Varela & I. Florescu & I. Sengupta, 2012. "Detecting market crashes by analysing long-memory effects using high-frequency data," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 623-634, April.
  • Handle: RePEc:taf:quantf:v:12:y:2012:i:4:p:623-634
    DOI: 10.1080/14697688.2012.664937
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    Cited by:

    1. Mariani, M.C. & Florescu, I. & SenGupta, I. & Beccar Varela, M.P. & Bezdek, P. & Serpa, L., 2013. "Lévy models and scale invariance properties applied to Geophysics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 824-839.
    2. Rodríguez-Aguilar, Román & Cruz-Aké, Salvador & Venegas-Martínez, Francisco, 2014. "A Measure of Early Warning of Exchange-Rate Crises Based on the Hurst Coefficient and the Αlpha-Stable Parameter," MPRA Paper 59046, University Library of Munich, Germany.

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