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

E. Barany, M. P. Beccar Varela, I. Florescu, I. SenGupta

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageEnglish
Pages (from-to)623-634
Number of pages12
JournalQuantitative Finance
Volume12
Issue number4
DOIs
StatePublished - Apr 2012

Keywords

  • Data sampled with high frequency
  • Detrended fluctuation analysis
  • Hurst parameter
  • Levy processes
  • Long memory effects
  • Truncated Levy flight

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