Modeling stylized facts for financial time series
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.
Cite
@article{arxiv.cond-mat/0401009,
title = {Modeling stylized facts for financial time series},
author = {M. I. Krivoruchenko and E. Alessio and V. Frappietro and L. J. Streckert},
journal= {arXiv preprint arXiv:cond-mat/0401009},
year = {2009}
}
Comments
Contribution to Proceedings of the Conference "Applications of Physics in Financial Analysis 4", Warsaw, 13-15 November, 2003. Four pages Elsevier LaTeX. Transparencies of the talk given by M.I.K. are attached, PostScript 32 pages. PDF file of the published contribution to the APFA4 Proceedings with removed misprints, introduced by a typesetter of physica A, is attached