Multivariate distribution of returns in 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). Free parameters of the model are fixed over the long term by fitting 100+ years of daily prices of the Dow Jones 30 Industrial Average. The multivariate probability density functions which we have constructed can be used for pricing derivative securities and risk management.
Keywords
Cite
@article{arxiv.cond-mat/0310300,
title = {Multivariate distribution of returns in financial time series},
author = {E. Alessio and V. Frappietro and M. I. Krivoruchenko and L. J. Streckert},
journal= {arXiv preprint arXiv:cond-mat/0310300},
year = {2007}
}
Comments
Talk given by M. I. Krivoruchenko at the International Conference of Computational Methods in Sciences and Engineering 2003 (ICCMSE 2003), Kastoria, Greece, 12-16 September 2003; 16 pages, 4 Postscript figures, REVTeX. Extended abstract appeared in: Proceedings of the International Conference of Computational Methods in Sciences and Engineering 2003 (ICCMSE 2003), Ed. T.E. Simos (World Scientific Publishing Co., Singapore, 2003), pp. 323-326