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 <italic>H</italic> parameter characterizing the self-similar property. We give an estimate of how close this model is to a self-similar model.
Year of publication: |
2012
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Authors: | Barany, E. ; Varela, M. P. Beccar ; Florescu, I. ; Sengupta, I. |
Published in: |
Quantitative Finance. - Taylor & Francis Journals, ISSN 1469-7688. - Vol. 12.2012, 4, p. 623-634
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Publisher: |
Taylor & Francis Journals |
Saved in:
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