Modeling stylized facts for financial time series

M. I. Krivoruchenko, E. Alessio, V. Frappietro and L. J. Streckert

Papers from arXiv.org

Abstract: Multivariate probability density functions of returns are constructed in order to model the empirical behavior of returns in a financial time series. They describe the well-established deviations from the Gaussian random walk, such as an approximate scaling and heavy tails of the return distributions, long-ranged volatility-volatility correlations (volatility clustering) and return-volatility correlations (leverage effect). The model is tested successfully to fit joint distributions of the 100+ years of daily price returns of the Dow Jones 30 Industrial Average.

Date: 2004-01, Revised 2004-11
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Published in Physica A 344, 263-266 (2004)

Downloads: (external link)
http://arxiv.org/pdf/cond-mat/0401009 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:cond-mat/0401009

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().