Detecting market crashes by analysing long-memory effects using high-frequency data
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.
Volume (Year): 12 (2012)
Issue (Month): 4 (April)
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