TY - JOUR
T1 - Detecting market crashes by analysing long-memory effects using high-frequency data
AU - Barany, E.
AU - Beccar Varela, M. P.
AU - Florescu, I.
AU - SenGupta, I.
PY - 2012/4
Y1 - 2012/4
N2 - 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.
AB - 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.
KW - Data sampled with high frequency
KW - Detrended fluctuation analysis
KW - Hurst parameter
KW - Levy processes
KW - Long memory effects
KW - Truncated Levy flight
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U2 - 10.1080/14697688.2012.664937
DO - 10.1080/14697688.2012.664937
M3 - Article
AN - SCOPUS:84859576066
SN - 1469-7688
VL - 12
SP - 623
EP - 634
JO - Quantitative Finance
JF - Quantitative Finance
IS - 4
ER -