Scaling and correlation in financial data
| dc.creator | Cont, Rama | |
| dc.date | 1997-05-08 | |
| dc.date | 1997-05-28 | |
| dc.date.accessioned | 2026-07-07T12:07:06Z | |
| dc.date.available | 2026-07-07T12:07:06Z | |
| dc.description | The statistical properties of the increments x(t+T) - x(t) of a financial time series depend on the time resolution T on which the increments are considered. A non-parametric approach is used to study the scale dependence of the empirical distribution of the price increments x(t+T) - x(t) of S&P Index futures, for time scales T, ranging from a few minutes to a few days using high-frequency price data. We show that while the variance increases linearly with the timescale, the kurtosis exhibits anomalous scaling properties, indicating a departure from the iid hypothesis. Study of the dependence structure of the increments shows that although the autocorrelation function decays rapidly to zero in a few minutes, the correlation of their squares exhibits a slow power law decay with exponent 0.37, indicating persistence in the scale of fluctuations. We establish a link between the scaling behavior and the dependence structure of the increments : in particular, the anomalous scaling of kurtosis may be explained by "long memory" properties of the square of the increments. | |
| dc.description | LATEX file + 8 postscript figures. | |
| dc.identifier | https://arxiv.org/abs/cond-mat/9705075 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/9705075 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/208854 | |
| dc.subject | Statistical Mechanics | |
| dc.subject | Adaptation and Self-Organizing Systems | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.subject | Data Analysis, Statistics and Probability | |
| dc.subject | Statistical Finance | |
| dc.title | Scaling and correlation in financial data | |
| dc.type | text |