A multi-time scale non-Gaussian model of stock returns
| dc.creator | Borland, Lisa | |
| dc.date | 2004-12-20 | |
| dc.date | 2005-01-24 | |
| dc.date.accessioned | 2026-07-07T12:07:03Z | |
| dc.date.available | 2026-07-07T12:07:03Z | |
| dc.description | We propose a stochastic process for stock movements that, with just one source of Brownian noise, has an instantaneous volatility that rises from a type of statistical feedback across many time scales. This results in a stationary non-Gaussian process which captures many features observed in time series of real stock returns. These include volatility clustering, a kurtosis which decreases slowly over time together with a close to log-normal distribution of instantaneous volatility. We calculate the rate of decay of volatility-volatility correlations, which depends on the strength of the memory in the system and fits well to empirical observations. | |
| dc.description | Comment added pertaining to volatility autocorrelation, clarifying approximation used in calculation | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0412526 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0412526 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/208839 | |
| dc.subject | Other Condensed Matter | |
| dc.subject | Statistical Finance | |
| dc.title | A multi-time scale non-Gaussian model of stock returns | |
| dc.type | text |