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

E. Barany, M. P. Beccar Varela, I. Florescu and I. Sengupta

Quantitative Finance, 2012, vol. 12, issue 4, 623-634

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 L�vy 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.

Date: 2012
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DOI: 10.1080/14697688.2012.664937

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